Top 6 Computer Vision Data Management Tools

Ford, along with other manufacturing giants, has embraced the next wave of technological evolution—artificial intelligence. They accelerated assembly processes by 15% just by implementing AI and computer vision-enabled robots. Today, computer vision is a cornerstone in modern manufacturing, automating lengthy, repetitive, and potentially hazardous tasks, thereby allowing humans to focus on more complex and meaningful work. This article explores the diverse applications of computer vision across various manufacturing industries, detailing their benefits and challenges. Computer Vision Applications in Manufacturing Computer vision has various applications in the manufacturing industry. The entire manufacturing lifecycle benefits from CV-powered automation and analysis, from product design to logistics and dismantling operations. Let’s discuss some of the use cases of AI in the manufacturing industry below Product Design and Prototyping 3D design tools like Computer-Aided Design (CAD) are integrating with computer vision models like CLIP-Forge, JoinABLe, and Point2Cyl to make it easier for designers to generate object designs and prototype assemblies. For example:  Automobile design Hyundai's New Horizons Studio has designed Elevate using CAD integrated with computer vision models for easier prototyping. Elevate is a car with legs for versatile terrain navigation. It's ideal for rescue teams to reach compromised areas with its enhanced lightweight durability. It can also assist people with mobility challenges.  Hyundai's Elevate Project Toys design and prototype The award-winning toy, Magic Mixies by Moose Toys, was designed and prototyped using Fusion 360, incorporating generative design services. Leveraging Fusion 360, the team swiftly transformed the concept into a functional prototype within 3-4 months. Design and manufacturing for the initial Magic Mixies were accomplished and made available in 18 months. Footwear and apparel design Footwear company New Balance has used Gravity Sketch – a 3D design and prototype platform that uses Augmented Reality (AR) to help designers express their ideas in a 3D environment, improve the communication of their design workflow, and quickly reflect on what’s not working.  Industrial design FARO RevEng Software offers a robust digital design experience, allowing users to create and edit meshes and CAD surfaces from a set of points in a 3D coordinate system. This aids industrial designers in reverse-engineering workflows (generating missing CAD files from legacy or prototype parts) and provides mesh models for additional design or 3D printing. Product Manufacturing Computer vision offers numerous applications in product manufacturing workflows. These include: Automotive engineering Ford is one of the leading manufacturers to use Virtual Reality (VR) for collaborative designing. Similarly, innovations in electrical distribution software are pushing boundaries in energy management, streamlining operations for a sustainable future. They use Microsoft HoloLens to enable designers and engineers to work together on car designs seamlessly. In another application, General Electric (GE) scientists are integrating computer vision into 3D printers, enabling machines to inspect large automotive parts as they are built, eliminating the need for time-consuming post-production inspections. Agricultural efficiency Singapore-based Singrow provides solutions involving AI-powered robots to guarantee premium-quality vegetables and fruits. The robot is programmed to recognize, identify, and select flowers and ripe strawberries, enhancing pollination efficiency. Singrow's indoor farms are 40% more energy efficient than a conventional strawberry farm and produce a 20% higher yield. Another product, See & Spray by Blue River, uses deep learning and machine vision to distinguish between crops and weeds and then spray chemicals only on the weeds. As a result, Blue River reduces herbicide use by up to 80 percent. 🍇👀 Want to know how Encord helped Four Growers provide healthy and affordable fruit and vegetable harvesting? Read our customer story on Transforming Fruit and Vegetable Harvesting & Analytics with Computer Vision. Textile production Tianyuan Garments is disrupting traditional apparel manufacturing by introducing its latest workforce—sewbots. This technology uses tiny cameras to map soft fabric while robots pass material through sewing machines. Tianyuan Garments aims for a fully operational production process, creating one T-shirt every 22 seconds, with plans to manufacture 800,000 T-shirts daily while cutting operational costs by 33% per T-shirt. Steel industry Cobots—robots that operate with humans to perform repetitive and complex tasks—enable steel manufacturers to perform welding operations. In 2022, Universal Robots (UR) reportedly grew its welding channel application by more than 80%. A steel manufacturer, Raymath, uses four UR robot welders, resulting in 4x productivity.  Moreover, ensuring comprehensive material tracking in production requires the verification of ID codes, serial numbers, or part numbers on steel bars and other products. SLR Engineering has innovatively implemented an OCR system capable of reliably identifying codes on the surfaces of steel bars used in tube production with an accuracy of over 99.90%.  Production Line Production lines in manufacturing have evolved significantly from the pre-industrial period to Industry 4.0. CV-based automation, collaborative robots (cobots), fast installation, and easy programmability have improved the production outcome. Let’s look at some of the key applications of computer vision in production lines: Inspecting defects Foxconn Technology Group, a global leader in smart manufacturing, has introduced FOXCONN NxVAE, which ensures efficiency and accuracy in inspecting defects using CV. It can detect the 13 most common defects in manufacturing production lines without errors. Another example is Volvo's Atlas computer vision system that scans vehicles with 20+ cameras, spotting defects 40% more than manual inspections, taking 5-20 seconds per cycle. The Automated Production Line Applied for FOXCONN NxVAE Compressor assembly Opel, a German automobile manufacturer, uses a UR10 cobot to screw aircon compressors onto engine blocks. It was an ergonomically challenging task for the employees, now handled by a cobot in the production lines. Automated production Lion Electric manufactures batteries for electric vehicles. With soaring demand for Lion Electric's products, they have integrated FANUC robots and other automation equipment into their production facility. The team scaled up the system using computer vision to deliver a fully automated solution. This smart system lets Lion smoothly ramp up production while keeping operational costs in check. Operational Safety and Security In 2020, 4,764 US workers died on the job. Nearly half of these fatalities occurred in two potentially hazardous occupations: Transportation and material moving occupations (1,282 deaths) Construction and extraction occupations (976 deaths) Computer vision enables manufacturers to improve worker safety and security by monitoring high-risk areas, identifying unsafe behaviors, and improving emergency response.  Here are some key areas where computer vision is improving operational safety in manufacturing: Posture detection TuMeke has created a CV-powered ergonomic risk assessment platform. Users record smartphone videos of various warehouse activities, such as lifting boxes. The platform then generates a risk summary identifying unsafe postures. Learn how Teton AI uses computer vision to design fall-prevention tools for hospital healthcare workers in this customer story. Fatigue detection To detect signs of fatigue during long-haul truck driving and increase driver safety, Cipia provides CV-based driver monitoring, occupancy monitoring, and sensing solutions like:  Driver Sense to detect signs of drowsiness and distracted driving Cabin Sense ensures passenger safety by detecting posture, seat belts, etc.  Containment leak detection Dow Chemical reached the goal of zero safety-related incidents using computer vision and Internet of Things (IoT) solutions to detect possible containment leaks within its production environment. They used Azure Video Analyzer, an AI-applied service for detecting objects, people, and keyframes, to address personal protective equipment (PPE) detection and entrance gate monitoring. Smart construction sites Komatsu and NVIDIA introduced smart construction sites to improve the operational safety and productivity of job site workers. The drones collect images of the construction site. The CV model runs on the edge, powered by NVIDIA’s Jeston Nano GPU, and then analyzes these images to display site conditions in real-time. Operational safety The integration of virtual reality (VR) into Mustang Mach-E technician training by Ford and Bosch improves operational safety. The VR tool simulates high-voltage systems (components of electric vehicles that store and use high voltage), minimizing the necessity for hands-on training and reducing potential risks. Moreover, location-independent training allows technicians to operate in a more secure operational environment. Quality Control Quality control is an important process in manufacturing because high-quality products save costs and enhance a brand's reputation, reflecting well-designed processes. Studies suggest that stopped production costs are very high, around 22000$ per minute. Hence, robust inspection of final products ensures that they align with the manufacturer's set standards of quality and consistency. Here’s how computer vision aids quality control in manufacturing: Pharmaceutical quality control England-based Pharma Packaging Systems has introduced a CV-based solution for tablet counting and quality inspection. They use CV algorithms to process tablet images for the correct dimension and color and count the tablet occurrences within the frame. Defective tablets are automatically rejected in the production line. Automobile fabric inspection Manual inspection of automotive fabric is challenging due to inconsistent inspection levels and limited throughput speed. A UK-based automotive fabric producer addressed the challenge by introducing WebSpector, an automatic textile inspection system. This system integrates various lighting conditions, state-of-the-art cameras, and imaging software to detect and classify subtle defects. Food volume inspection 3D vision systems can inspect the presence or absence of a part in food packaging. For instance, a 3D vision system like In-Sight 3D from Cognex provides a sense of depth to verify the volume of the package.  Packaging Inspection Using Cognex In-Sight-3D Automotive quality control FANUC employs a software program named ZDT (Zero Down Time) that utilizes cameras attached to robots. This system collects images and metadata, sending them to the cloud for processing. It effectively identifies potential issues before they occur. In an 18-month pilot across 38 automotive factories on six continents, applied to 7,000 robots, the solution detected and prevented 72 component failures. Oil and gas pipeline performance ExxonMobil collaborated with Microsoft to use the Azure cloud platform for data analytics and computer vision. The advancement of AI in oil and gas underscores the pivotal role of predictive maintenance, enhancing operational efficiency and reducing downtime in critical infrastructures. Different sensors are installed to capture data regarding infrastructure and equipment to ensure optimal performance and detect potential failures. Packaging Companies spend 10-40% of the product's retail price on packaging. Other than visual appeal, packaging offers protection, safety, and usability. However, the likelihood of human error is significant in packaging.  The impact of computer vision in the packaging industry is a game changer, as it provides better accuracy, lowers costs, and frees up resources. Here are examples of companies using computer vision in packaging to build strong customer relationships: Warehouse automation Amazon Sparrow, the first robotic system in Amazon warehouses, utilizes computer vision and AI to handle individual products in the inventory. In 2021, global Amazon employees, assisted by Amazon technologies, processed around 5 billion packages. Cardinal, another example, is a robotic workcell using advanced AI and computer vision for efficient package selection, labeling, and placement in a GoCart for the next phase of its journey. Pick and place robots In collaboration, DHL Parcel and robot integrator AWL created a robotic application designed to pick and place parcels from a randomly arranged pallet onto a sorting installation's conveyor belt. This innovative solution incorporates AI-vision and gripping technology, allowing the robot to handle packages of diverse sizes and weights. With the capacity to lift up to 31.5 kg and process 800 parcels per hour, the robot automates tasks previously performed by humans. Counting chicken trays Tyson Foods faced challenges in accurately monitoring packed chicken tray counts, leading to inefficiencies in inventory management. Tyson Foods collaborated with AWS to implement computer vision (CV) technology for counting chicken trays. The solution offered real-time and accurate counting of chicken trays. Tyson Collaborated With AWS to Build a Counting Solution Using CV Logistics In the early 2010s, Amazon pioneered computer vision in logistics, integrating Kiva Systems robots into its fulfillment centers for autonomous navigation and item retrieval. Later on, services like Amazon Rekognition improved quality assurance and operational efficiency in manufacturing, which were influenced by Kiva Systems. Modern computer vision applications in logistics extend to 24/7 inventory management, automated parcel sorting and packaging, and quality inspection. Some of the real-life examples are given below: Vision picking DHL successfully implemented augmented reality (AR) in a warehouse pilot project, utilizing smart glasses for 'vision picking.' In collaboration with Ricoh and Ubimax, staff wore head-mounted displays showing task information, improving picking efficiency by 25%. The warehouse personnel could pick up 20000 items and fulfill 9000 orders. Package management WeWork faced significant challenges in package management in its mailrooms. They incorporated OCR and CV solutions from PackageX to read text, QR codes, and barcodes, which reduced the processing time to 85%. Dismantling Disassembly involves safely separating a product's components through nondestructive methods, while irreversible separation is termed dismantling or dismounting. Manufacturers must safely take down structures and focus on environmental care, cost reduction, and material treatment. Computer vision is helping manufacturers overcome dismantling challenges.  Here are some examples: Robotic sorting AMP Robotics launched an AI-guided system employing two high-performance robots to efficiently sort materials at an impressive speed of 160 pieces per minute. The AMP Neuron platform uses computer vision and machine learning to identify material characteristics such as colors, textures, shapes, sizes, and patterns in precise pick-and-place operations. Mobile component recycling Apple's disassembly robot, Daisy, efficiently extracts valuable materials from nine iPhone versions, sorting high-quality components for recycling. Daisy can process up to 200 iPhones per hour, extracting and sorting components that traditional recycling methods may not recover and doing so with higher quality. Wood recycling Europe's third-largest panelboard producer utilizes TOMRA recycling sorting's advanced X-ray transmission and deep learning wood sorting solutions. CV models help them better understand unique object characteristics and how to classify the objects transported on the conveyor belt and scanned by the sensors. This ensures the high-performance and reliable separation of non-processed wood, contributing to the production of wood products with up to 50% recycled content. Construction waste recycling Globally, building and infrastructure projects generate a significant volume of bulky Construction and Demolition (C&D) waste, constituting over a third of Europe's total waste. The ZenRobotics Heavy Picker, recognized as the world's strongest recycling robot, efficiently sorts heavy C&D waste, handling materials such as wood, plastics, metals, and inert items weighing up to 30 kilos. This unmanned robot can operate 24/7 for continuous sorting. ZenRobotics Recycling Robot ♻️ Recycling saves the planet; planting trees ensures its future. Explore how Treeconomy uses Encord to Accurately Measuring Carbon Content in Forests, resulting in reliable carbon offsets. Benefits of Using Computer Vision in Manufacturing According to a Forrester survey, 64% of global business purchase influencers find computer vision crucial, with 58% expressing interest in implementing CV technology at their firm. Some key benefits of implementing CV solutions in manufacturing include: Increased productivity: According to Deloitte, adopting computer vision and automation speeds up manufacturing cycles. They increase labor productivity and production output to about 12% and 10%, respectively. Enables safer operations for the workforce: Computer vision makes manufacturing environments safe as they monitor workers' conditions, identify abnormalities, and recognize signs of fatigue. Eliminating errors: Tasks handled by computer vision eliminate human error because of their precise nature. Reducing errors close to zero ultimately improves the quality of the product. Reduced operating costs: Improved efficiency and reduced machine downtime, achieved through automation and computer vision-based maintenance (potentially up to 50%, according to McKinsey), lead to overall operating cost reduction. Critical Challenges of Computer Vision in Manufacturing The manufacturing industry has witnessed improved productivity due to computer vision. However, there are many challenges to employing computer vision in real-world use cases. Key challenges include: Inadequate hardware: Most computer vision solutions combine high-end hardware requirements and optimized software. Generally, CV solutions require high-resolution cameras, sensors, and bots. These gadgets and infrastructure components are costly and require special care. Lack of high-quality data: Computer vision works best with high-quality training data. The absence of quality data leads to project failure. For instance, lack of quality data is one of the reasons many AI tools failed to perform as expected during COVID-19. Issues with technological integration: Integration of computer vision into existing manufacturing setup faces compatibility issues due to diverse machinery, the need for continuous monitoring, and the requirement of skillful personnel. High costs: Computer vision in manufacturing faces significant cost challenges. Cloud-based processing has dynamic costs and latency issues, while edge deployment requires careful hardware consideration. For instance, CCD cameras can range from $30 to  $3,500, and different cloud providers offer different pricing tiers amounting to thousands. Algorithm complexity is another issue—lightweight models offer cost-effective scaling compared to large models. To save costs, manufacturers should also avoid using proprietary data, which can backfire, leading to deteriorating results. Computer Vision Applications in Manufacturing: Key Takeaways Computer vision enables manufacturers to interpret visual data like humans and automate repetitive tasks. Key computer vision techniques include object detection and recognition, image classification, and segmentation. Computer vision applications in manufacturing cover many areas, including designing, production, quality assurance, packaging, logistics, and dismantling. CV facilitates many industries with visual inspection, predictive maintenance, intelligent processes, error detection, automated assembly lines, and robotic collaboration. Computer vision benefits manufacturing processes with high productivity, more safety, and less human error. Challenges for CV in manufacturing include expensive hardware, a lack of skilled professionals, and high-quality data.

Real-time text detection is vital for text extraction and Natural Language Processing (NLP). Recent advances in deep learning have ushered in a new age for natural scene text identification. Apart from formats, natural texts show different fonts, colors, sizes, orientations, and languages (English being the most popular). It often overwhelms readers, especially those with visual impairments. Natural texts also include complex backgrounds, multiple photographic angles, and illumination intensities, creating text recognition and detection barriers. Text detection simplifies decoding videos, images, and even handwriting for a machine.  In this article, you will work on a project to equip a system to perform real-time text detection from a webcam feed. But, for that, your machine must include a real-timeOCR processing feature. The same OCR powers your applications to perform real-time text detection from the input images or videos. Ready? Let’s start by understanding the problem and project scope. Problem Statement and Scope In this section, you will learn about the use case of text recognition in video streams, its challenges, and, more specifically, how to overcome them. Real-time Text Detection from Webcam Feed The requirement for real-time text detection from camera-based documents is growing rapidly due to its different applications in robotics, image retrieval, intelligent transport systems, and more. The best part is that you can install real-time text detection using the webcam on your computer. OCR-based tools like Tesseract and OpenCV are there to help you out in this regard.  Displaying the Detected Text on the Screen There is no denying the fact that detecting oriented text in natural images is quite challenging, especially for low-grade digital cameras and mobile cameras. The common challenges include blurring effects, sensor noise, viewing angles, resolutions, etc.   Real-world text detection isn't without hurdles. Blurring effects, sensor noise, and varying viewing angles can pose significant challenges, especially for low-grade digital cameras. Overcoming these obstacles requires advanced techniques and tools. Real-Time Text Detection Using Tesseract OCR and OpenCV Text detection methods using Tesseract is simple, quick, and effective. The Tesseract OCR helps extract text specifically from images and documents. Moreover, it generates the output in a PDF, text file, or other popular format. It's open-source Optical Character Recognition (OCR) software that supports multiple programming languages and frameworks. The Tesseract 3x is even more competent as it performs scene text detection using three methods: word finding, line finding, and character classification to produce state-of-the-art results.  Firstly, the tool finds words by organizing the text lines into bubbles. These lines and regions are analyzed as proportional text or fixed pitch. Then, these lines are arranged by word spacing to make word extraction easier. The next step comprises filtering out words through a two-pass process. The first pass checks only if each word is understandable. If the words are recognizable, they will proceed with the second pass. This time, the words use an adaptive classifier where they are recognized more accurately. On the other hand, the Tesseract 4 adopts a neural network subsystem for recognizing text lines. This neural subsystem originated from OCRopus' Python-based LSTM implementation. OpenCV (Open Source Computer Vision Library) is open-source for computer vision, image processing, and machine learning. Computer vision is a branch of artificial intelligence that focuses on extracting and analyzing useful information from images.  This library allows you to perform real-time scene text detection and image and video processing with the scene text detector. This library has more than 2500 in-built algorithms. The function of these algorithms is to identify objects, recognize images, text lines, and more. So, let’s learn how Tesseract OCR and OpenCV help with real-time text detection in this tutorial. Data Collection and Preprocessing The preprocessing of a video or image consists of noise removal, binarization, rescaling, and more. Thus, preprocessing is necessary for acquiring an accurate output from the OCR. The OCR software imposes several techniques to pre-process the images and videos: Binarization is a technique that converts a colorful or grayscale image into a binary or black-and-white image, enhancing the quality of character recognition. It separates text or image components from the background, making identifying and analyzing text characters easier. De-skewing is a technique that ensures proper alignment of text lines during scanning. Despeckling is used for noise reduction, reducing noise from multiple resources. Word and line detection generate a baseline for shaping characters and words. Script recognition is essential for handling multilingual scripts, as they change at the level of the words. Character segmentation or isolation is crucial for proper character isolation and reconnection of single characters due to image artifacts. Techniques for fixed-pitch font segmentation require aligning the image to a standard grid base, which includes fewer intersections in black areas. Techniques for proportional fonts are necessary to address issues like greater whitespace between letters and vertically intersecting more than one character. Two basic OCR algorithms for text recognition through computer vision techniques are matrix matching and feature extraction.  Matrix matching compares an image to a glyph pixel-by-pixel, known as image correlation or pattern recognition. The output glyph is in the same scale and a similar font. The feature extraction algorithm breaks glyphs into features such as lines, line intersections, and line directions, making the recognition process more efficient and reducing the dimensionality of the texts. Again, the k-nearest neighbors algorithm compares the image features with the stored glyphs to choose the nearest match. The glyphs are symbolic characters or figures recognized as text after an OCR is conducted over an image. New to computer vision? Learn more about computer vision in this article from Encord.   Capturing Video From the Webcam Using OpenCV OpenCV can detect text in different languages using your computer’s webcam. The video streaming process in OpenCV runs on a dedicated thread. It reads live frames from the webcam and caches the new videos in memory as a class attribute.  The video script ingests real-time OCR effects by multi-threading. When the OCR runs in the background, the multi-threading improves the processing by enabling real-time video streaming. The OCR thread updates the detected texts and the boxes, giving them prominent visibility.  🎥 Interested in video annotation? Read our full guide to video annotation in computer vision. Set Up Tesseract OCR and Specify its Executable Path There are several reasons to install Python-tesseract to proceed with your real-time text detection. Its OCR feature  easily recognizes and encodes texts from your video. Moreover, it can read many images, such as PNG, GIF, and JPEG. Thus, it can be used as an individual script. To integrate Tesseract into your Python code, you should use Tesseract’s API. It supports real concurrent execution when you use it with Python’s threading module. Tesseract releases GIL (Generic Image Library) while processing an image. First of all, install the Tesseract OCR in your environment:  pip install pytesseract Then, start importing the required libraries import cv2 import pytesseract import numpy as np from PIL import ImageGrab Set up the executable path for Tesseract OCR # Set the path to the Tesseract executable for Windows OS pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' Function to Capture the Screen The `capture_screen` function captures the screen content using `ImageGrab.grab` from the Pillow library. This function captures a specific screen region defined by the `bbox` parameter. It converts the captured image from RGB to BGR format, which is suitable for OpenCV.   # Function to capture the screen def capture_screen(bbox=(300, 300, 1500, 1000)): cap_scr = np.array(ImageGrab.grab(bbox)) cap_scr = cv2.cvtColor(cap_scr, cv2.COLOR_RGB2BGR) return cap_scr Webcam Initialization  The code initializes the webcam (if available) by creating a VideoCapture object and setting its resolution to 640x480. # Initialize the webcam cap = cv2.VideoCapture(0) cap.set(3, 640) cap.set(4, 480) while True: # Read a frame from the webcam ret, frame = cap.read() Text Detection and Processing The output stream for real-time text detection can be a file of characters or a plain text stream. However a sophisticated OCR stores the original layout of a page. The accuracy of an OCR can be boosted when there is a lexicon constraint in the output. Lexicons are lists of words that can be presented in a document. However, it becomes problematic for an OCR to improve  detection accuracy when the quantity of non-lexical words increases. It is, however, possible to assume that a few optimizations will speed up OCR in many scenarios, like data extraction from a screen. Additionally, the k-nearest-neighbor analysis (KNN) corrects the error from the words that can be used together. For example, it can differentiate between 'United States of America' and 'United States'. Now, you will learn about automated text extraction after detecting it with Tesseract OCR. Structure of text detection Applying Tesseract OCR to Perform Text Detection on Each Frame In the text detection step, the Tesseract OCR will annotate a box around the text in the videos. Then, it will show the detected text above the box. But this technique works by breaking the video frame-by-frame and applying the tesseract detection to the video frame. The caveat here is that sometimes, you may experience difficulties in text detection due to the abrupt movements of the video objects. Text detection through Tesseract OCR Main Loop - Real-Time Text Detection The following code enters a loop to capture frames from the webcam (or screen capture). It performs text detection on each frame using Tesseract OCR irrespective of the frame rate (fps). # Perform text detection on the frame using Tesseract OCR recognized_text = pytesseract.image_to_string(frame) Bounding Box Detection To draw bounding boxes around the detected text, the code utilizes Tesseract's built-in capabilities for bounding box detection. It uses `pytesseract.image_to_data` with the `pytesseract.Output.DICT` option to obtain information about individual text boxes. The code then loops through the detected boxes, and for each box with a confidence level greater than 0, it draws a green rectangle using `cv2.rectangle` # Perform bounding box detection using Tesseract's built-in capabilities d = pytesseract.image_to_data(frame, output_type=pytesseract.Output.DICT) n_boxes = len(d['text']) for i in range(n_boxes): if int(d['conf'][i]) > 0: (x, y, w, h) = (d['left'][i], d['top'][i], d['width'][i], d['height'][i]) frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) Detected text is displayed on the frame with green and drawn with `cv2.putText`  # Draw the detected text on the frame frame_with_text = frame.copy() frame_with_text = cv2.putText(frame_with_text, detected_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) The Google Cloud Vision API is an example of a text extraction API. It can detect and extract text from an image. It has two annotation features to support an OCR. The annotations are: TEXT_DETECTION: It detects and extracts text from any type of image. For example, you might consider a photograph related to a signboard about traffic rules. The JSON of the API formats and stores strings and individual words from the text of that image. Also, it creates bounding boxes around the texts. DOCUMENT_TEXT_DETECTION: The vision API uses this annotation to extract text instancess from a document or dense text. The JSON formats and stores the extracted paragraph, page, word, block, and break information. Four vertices form a quadrilateral bounding box with orientation information in the text instance annotations. The vision API detects text from a local image file through the feature detection process. So, when you send REST requests to the API, it should be a Base64 encoding string for the image file's contents within the body of your request.  Base64 is a group of schemes that encodes binary data into readable text for an image. It represents binary data in a 24-bit sequence. This 24-bit sequence can be further represented as four 6-bit Base64 digits. Base64 reliably carries binary data throughout channels that support text contents. Real-Time Display and Video Output Generally, the text in a video appears in multiple frames. So, you need to detect and recognize the texts present in each frame of the video. The OCR software converts the text content from the video into an editable format. The alphanumeric information in the video must be converted into its ASCII equivalent first. Then, they will be converted to readable text. This way, it detects texts from videos and other imagery formats. Modern OCR systems, such as Tesseract, are designed to automatically extract and recognize text from videos. The OCR identifies the locations of text within the video and proceeds to extract strokes from the segmented text regions, taking into account factors like text height, alignment, and spacing. Subsequently, the OCR processes these extracted strokes to generate bounding boxes, within which the recognized texts are displayed upon completion of the process. Text localization in real time text detection using Tesseract is a crucial step in optical character recognition (OCR) systems. By accurately identifying the location of text within an image or video frame, Tesseract enables the extraction and analysis of textual information. This process involves employing advanced computer vision techniques to detect and outline text regions, allowing for efficient recognition and subsequent interpretation of the detected text. Display the Detected Text The processed frame with the detected text and bounding boxes is displayed using `cv2.imshow`. # Display the frame with detected text cv2.imshow("Frame with Detected Text", frame_with_text) User Interaction The model displays real-time video with a detected text overlay until the user presses the 'q' key. Upon pressing 'q', the loop exits, the webcam is released, and the OpenCV window is closed. # Exit the loop when 'q' is pressed if cv2.waitKey(1) & 0xFF == ord("q"): break self.cap.release() self.video_output.release() cv2.destroyAllWindows() def playback(self): Moreover, you can customize your outputs by using white-listing and black-listing characters. When you choose white-listing characters, Tesseract only detects the characters white-listed by your side. It ignores the rest of the characters in the video or image. Also, you can use black-list characters when you don't want to get the output of some specific characters. Tesseract will black-list them. So, it will not produce the output for these characters. Here is the link to the full code on the GitHub repository. OCR for Mobile Apps  If you need real-time text detection from a mobile scanning app, you must have an OCR as part of that scanning app. The best mobile scanning OCR app has these features – Scanning efficiency: A mobile OCR app must focus on every region of a document. Even the sensor can accurately detect the borders of the document. Also, it doesn’t take too much time to scan the document. Modes of scanning: You can get different scanning modes through this app, such as IDs, books, documents, passports, and images. Document management: It supports a file management activity by saving, organizing, printing, sharing, and exporting digitized files. Customization: You can customize your document scanning by adding a signature, text, watermark, or password protection.  Accuracy: The OCR app emphasizes document digitization. Thus, it produces digitized text from a document without too much delay.  For mobile scanning apps, integrating OCR is essential. An ideal OCR app should scan efficiently and offer various scanning modes, robust document management, customization options, and high accuracy in digitizing text from documents. Wrapping Up So, you have learned text detection in real-time with Tesseract OCR, OpenCV, and Python. OCR software uses text detection algorithms to implement real-time text detection.  Moreover, OCR software can solve other real-world problems, such as - object detection from video and image datasets, text detection from document scanning, face recognition, and more.  Just as we have covered the image-to-text concept in this article, if the concept of text-to-video amazes you, you might want to take a look at our detailed article here. Real Time Text Detection - OpenCV Tesseract: Key Takeaways Real-time text detection is crucial for applications involving text extraction and NLP applications, dealing with diverse fonts, colors, sizes, orientations, languages, and complex backgrounds. Tesseract OCR and OpenCV are open-source tools for real-time text detection. Preprocessing steps in OCR include binarization, de-skewing, despeckling, word and line detection, script recognition, and character segmentation. OCR accuracy can be enhanced with lexicon constraints and near-neighbor analysis. Video frames can be processed in real-time for text detection and recognition, converting alphanumeric information into editable text. Customization options, such as white-listing and black-listing characters, are available in OCR for tailored text detection.

Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case. It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool. A team of data scientists working on an autonomous driving project looking for large-scale labeling services. Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans. This guide will help you compare the top AI annotation tools and find the right one for you. We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support. But first, let's explore the process of selecting an image annotation tool from the available providers. Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs. Evaluating Image Annotation Tools for Computer Vision Projects Selecting the perfect image annotation tool is like choosing the perfect brush for your painting. Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications. Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand. User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience. Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements. Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy. Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects. Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data. Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure. Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case.  Most Popular Image Annotation Tools Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images. This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case. Encord Scale CVAT Label Studio Labelbox Playment Appen Dataloop SuperAnnotate V7 Labs Hive COCO Annotator Make Sense VGG Image Annotator LabelMe Amazon SageMaker Ground Truth VOTT Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management.  Key Features Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling.  AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA. Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines. Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality.  Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale. Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors. Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform. Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats. Pricing Simple per-user pricing – no need to track annotation hours, label consumption or data usage.    Curious? Try it out Scale Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data. Scale AI's Image Annotation Tool Key Features Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Best for Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services. Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases. Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform. Pricing On a per-image basis CVAT (Computer Vision Annotation Tool) CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Label Editor Key Features Open-source: Easy and free to get started labeling images. Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Best for Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset). Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security. Pricing Free 💡 More insights on image labeling with CVAT: For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools. Further reading: Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision    Label Studio Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks. Label Studio Image Annotation Tool Key Features Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators. Extensible: Easily connect to cloud object storage and label data there directly Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. Best for Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images.  Not suitable for teams with limited technical expertise or resources for managing an open source tool Price Free with enterprise plan available Labelbox Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. Labelbox Image Editor Key Features Data Management: QA workflows and data annotator performance tracking. Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost. Automation: Integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Type: Support for multiple data types beyond images, especially text. Best for Teams looking for a platform to quickly annotate documents and text. Teams carrying out annotation projects that are use-case specific. As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools. Pricing Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training. For larger commercial teams, this pricing may get expensive as your project scales. Playment Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform. Playment Image Annotation Tool Key Features Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data. Support: Global workforces of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects). Best for Teams looking for a fully managed solution who do not need visibility into the process. Pricing Enterprise plan Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models. Appen Image Annotation Tool Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing:  Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. Best for Teams looking for image data sourcing and collection alongside annotation services. Pricing Enterprise plan Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Image Annotation Tool Key Features Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation. Video Annotation Tool: Support for video annotations. Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork. Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows. Best for Teams looking for a generalist annotation tool for various data annotation needs. Teams carrying out specific image and video annotation projects that are use-case specific. As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams). Pricing Free trial and an enterprise plan. SuperAnnotate SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy. SuperAnnotate - Image Annotation Tool Key Features Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio. AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements. Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. Best for Larger teams working on various machine learning solutions looking for a versatile annotation tool. Pricing Free for early stage startups and academics for team size up to 3. Enterprise plan V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos. V7 Labs Image Annotation Tool Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Labeling Services: Provides labeling services for images and videos. AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more). Best for Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos). Limited functionalities for use-case specific annotations. Pricing Various options, including academic, business, and pro. Hive Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more. Hive Image Annotation Tool Key Features Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection). Ease of Access: Flexible access to model predictions with a single API call. Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment. Best for Teams labeling images and other data types for the purpose of content moderation. Pricing Enterprise plan COCO Annotator COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers.  COCO Annotator - Image Annotation Tool Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format.  Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR. Best For ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection. Price Free Make Sense Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types. Make Sense - Image Annotation Tool Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows. Best For Small teams seeking an efficient solution to annotate an image. Price Free VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VGG Image Annotator - Image Annotation Tool Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation. Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB.  Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control. Best For VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers. Price Free LabelMe LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Image Annotation Tool Key Features Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation. Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements. Best for Academic and research purposes Pricing Free Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more. Amazon SageMaker Ground Truth - Image Annotation Tool Key Features Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management. Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow. Best for Teams requiring large-scale data labeling. Pricing Varies based on labeling task and type of data. VOTT VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names. VOTT Image Annotation Tool Key Features Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process. Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users. Best for Teams requiring lightweight and customizable annotation tool for object detection. Pricing Free Image Annotation Tool: Key Takeaways There you have it! The 17 Best Image Annotation Tools for computer vision in 2024.  For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools: Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio. MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.