Open Source Image Recognition Software
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Image Recognition Software
View 63 business solutionsBrowse free open source Image Recognition software and projects below. Use the toggles on the left to filter open source Image Recognition software by OS, license, language, programming language, and project status.
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Tesseract OCR
Open Source OCR Engine
Tesseract is an open source OCR or optical character recognition engine and command line program. OCR is a technology that allows for the recognition of text characters within a digital image. With the latest version of Tesseract, there is a greater focus on line recognition, however it still supports the legacy Tesseract OCR engine which recognizes character patterns. Tesseract can recognize over 100 languages out-of-the-box, and can be trained to recognize other languages. It supports various output formats, including plain text, HTML, PDF and more. It also has unicode (UTF-8) support. -
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LabelImg
Graphical image annotation tool and label object bounding boxes
LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besides, it also supports YOLO and CreateML formats. Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended. Virtualenv can avoid a lot of the QT / Python version issues. Build and launch using the instructions. Click 'Change default saved annotation folder' in Menu/File. Click 'Open Dir'. Click 'Create RectBox'. Click and release left mouse to select a region to annotate the rect box. You can use right mouse to drag the rect box to copy or move it. The annotation will be saved to the folder you specify. You can refer to the hotkeys to speed up your workflow. -
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Computer Vision Annotation Tool (CVAT)
Interactive video and image annotation tool for computer vision
Computer Vision Annotation Tool (CVAT) is a free and open source, interactive online tool for annotating videos and images for Computer Vision algorithms. It offers many powerful features, including automatic annotation using deep learning models, interpolation of bounding boxes between key frames, LDAP and more. It is being used by its own professional data annotation team to annotate millions of objects with different properties. The UX and UI were also specially developed by the team for computer vision tasks. CVAT supports several annotation formats. Format selection can be done after clicking on the Upload annotation and Dump annotation buttons. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including Classical CNN (VGG AlexNet GoogleNet Inception), Face Detection (MTCNN RetinaFace), Segmentation (FCN PSPNet UNet YOLACT), and more. ncnn is currently being used in a number of Tencent applications, namely: QQ, Qzone, WeChat, and Pitu. -
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Tesseract.js
A pure Javascript Multilingual OCR
Tesseract.js is a pure Javascript port of the popular Tesseract OCR engine. Tesseract.js' library supports more than 100 languages, automatic text orientation and script detection, a simple interface for reading paragraph, word, and character bounding boxes. Tesseract.js can run either in a browser and on a server with NodeJS. Tesseract.js is a javascript library that gets words in almost any spoken language out of images. The main Tesseract.js functions (ex. recognize, detect) take an image parameter, which should be something that is like an image. What's considered "image-like" differs depending on whether it is being run from the browser or through NodeJS. -
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openalpr
Automatic license plate recognition library
Deploy license plate and vehicle recognition with Rekor’s OpenALPR suite of solutions designed to provide invaluable vehicle intelligence which enhances business capabilities, automates tasks, and increases overall community safety! Rekor’s OpenALPR suite of solutions utilizes artificial intelligence and machine learning to greatly surpass legacy OCR solutions. Now, in real-time, users can receive a vehicle's plate number, make, model, color, and direction of travel. Rekor’s OpenALPR suite of solutions allows law enforcement and homeowners to protect their communities, while businesses can boost customer loyalty by receiving alerts the moment a plate of interest is detected. Rekor’s OpenALPR suite of solutions is a force multiplier. Rekor Scout™ upgrades nearly any IP, traffic, or security camera to give you an immediate edge, while Rekor CarCheck analyzes vehicle images and returns valuable data for countless business use-cases. -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common text files. For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. AI analysis runs read-only, and all suggestions must be reviewed before being applied. AI File Sorter can run fully offline using local models like Mistral or LLaMA, so files and metadata stay on your device unless you configure a remote endpoint. -
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OpenFace Face Recognition
Face recognition with deep neural networks
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources. Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark. -
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Mozilla JPEG Encoder Project
Improved JPEG encoder
MozJPEG improves JPEG compression efficiency achieving higher visual quality and smaller file sizes at the same time. It is compatible with the JPEG standard, and the vast majority of the world's deployed JPEG decoders. MozJPEG is compatible with the libjpeg API and ABI. It is intended to be a drop-in replacement for libjpeg. MozJPEG is a strict superset of libjpeg-turbo's functionality. All MozJPEG's improvements can be disabled at run time, and in that case it behaves exactly like libjpeg-turbo. MozJPEG is meant to be used as a library in graphics programs and image processing tools. We include a demo cjpeg command-line tool, but it's not intended for serious use. We encourage authors of graphics programs to use libjpeg's C API and link with MozJPEG library instead. Progressive encoding with "jpegrescan" optimization. It can be applied to any JPEG file (with jpegtran) to losslessly reduce file size. -
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Jimp
An image processing library written entirely in JavaScript for Node
An image processing library for Node written entirely in JavaScript, with zero native dependencies. If you're using this library with TypeScript the method of importing slightly differs from JavaScript. Instead of using require, you must import it with ES6 default import scheme. If you're using a web bundles (webpack, rollup, parcel) you can benefit from using the module build of jimp. Using the module build will allow your bundler to understand your code better and exclude things you aren't using. If you're using webpack you can set process.browser to true and your build of jimp will exclude certain parts, making it load faster. The static Jimp.read method takes the path to a file, URL, dimensions, a Jimp instance or a buffer and returns a Promise. In some cases, you need to pass additional parameters with an image's URL. -
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NSFWJS
Client-side indecent content checking powered by TensorFlow.js
NSFWJS is a simple JavaScript library that can quickly and quite accurately identify NSFW images, all in the client's browser. It is powered by TensorFlow.js and the NSFW detection model, and delivers around 90% accuracy that is improving each time. NSFWJS classifies images with percentages under five categories, namely: drawing and neutral, which are both safe for work; sexy, which includes sexually explicit images; and hentai and porn, which are pornographic drawings and images. NSFWJS offers a 'browserified' version, an NSFW filter web extension that filters out NSFW images from your browser, and also has a separate React Native app. -
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html2canvas
A JavaScript HTML screenshot renderer
html2canvas is a JavaScript HTML renderer. The script provides you with the tools to take screenshots of webpages directly on the browser. The screenshot is based on the DOM and therefore, it may not be 100% accurate to the real representation, given that it is not an actual screenshot, but a type of screenshot built based on the available data and information of the page. The script renders such page as a canvas image, by reading the DOM and the different styles of the featured elements. It doesn't require rendering from the server, given that the image is created on the user's browser. However, as it is heavily dependent on the browser, the library is not to be used in nodejs. It can't circumvent any browser content policy restrictions and to render cross-origin content a proxy will be needed to get the content to the same origin. -
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Color Thief
Grab the color palette from an image using just Javascript
The Color Thief package includes multiple distribution files to support different environments and build processes. Gets the dominant color from the image. Color is returned as an array of three integers representing red, green, and blue values. When called in the browser, the image argument expects an HTML image element, not a URL. When run in Node, this argument expects a path to the image. quality is an optional argument that must be an Integer of value 1 or greater, and defaults to 10. The number determines how many pixels are skipped before the next one is sampled. We rarely need to sample every single pixel in the image to get good results. The bigger the number, the faster a value will be returned. Gets a palette from the image by clustering similar colors. The palette is returned as an array containing colors, each color itself an array of three integers. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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MMDetection
An open source object detection toolbox based on PyTorch
MMDetection is an open source object detection toolbox that's part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. It stems from the codebase developed by the MMDet team, who won the COCO Detection Challenge in 2018. Since that win this toolbox has continuously been developed and improved. MMDetection detects various objects within a given image with high efficiency. Its training speed is comparable or even faster than those of other codebases like Detectron2 and SimpleDet. It supports multiple detection frameworks right out of the box, as well as various backbones and methods. -
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Deface GUI - Face Anonymization Tool
Graphical User Interface Face Anonymization Tool
This application is a professional tool with a graphical user interface that enables anonymization of faces using the Deface Engine. Cross-Platform Compatible (Linux-Windows) NOTE: To use on Windows, first install Python. Then, if necessary, install “pip install deface” (only if necessary). -
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Exadel CompreFace
Leading free and open-source face recognition system
Exadel CompreFace is a free and open-source face recognition GitHub project. Essentially, it is a docker-based application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use CompreFace. The system provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition. The solution also features a role management system that allows you to easily control who has access to your Face Recognition Services. CompreFace is delivered as a docker-compose config and supports different models that work on CPU and GPU. Our solution is based on state-of-the-art methods and libraries like FaceNet and InsightFace. Official website: https://exadel.com/solutions/compreface/ Github link: https://github.com/exadel-inc/CompreFace -
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Img2Txt
Img2Txt - Extract Text From Images using AI
Important: If you are sharing this program. Please Include the official Download Link What is Img2Txt? Img2Txt is a Python-based application packaged using PyInstaller that utilizes the power of pytesseract, an AI-powered optical character recognition (OCR) library, to extract text from images and convert it into plain text. The application features a simple and modern user-friendly interface created using customtkinter, allowing users to easily process images and obtain the text within them. Support me at : https://www.buymeacoffee.com/zsynctic it will motivate me and it will make me create more projects Support For any questions or issues, please open an issue on the Img2Txt GitHub repository. Warning: When running Img2Txt.exe a Blue Window Might Popup. To Run The Application You Have To Press More Info And Then Run Anyways. © zSynctic -
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Image to Text
Convert an image to text to spot intelligible words.
The program will convert to text an image, such as a photo , with the purpose of analyzing it to spot intelligible words. Use the program with photos of clouds, sea, soil, vegetation or any other photo of natural or man-made semi-homogeneous configuration, to reveal the hidden universal-philosophical messages of the image. You can also use it on photos of people or art pieces to have a psychological insight of the person portrayed or of the image author. The resulting text will be a long string of mostly repeating characters but, every so often, you will notice an intelligible word, either spelled normally or with repeated letters, missing letters or anagrammed letters. -
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Wildlife Classifier
Tool for classifying & evaluating wildlife species from their images
A simple interactive application that classifies a wide variety of wildlife species including mammals, marine animals, birds, insects, reptiles, and a few plants from their images and displays their ecological roles. The tool is accurate, compact, and does not require internet connectivity. -
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ANDTool
Analysis Nuclei DAB (AND) Tool
Analysis Nuclei DAB (AND) Tool is a Graphical User Interface (GUI) to analyse microscopy images representing cells with nuclei stained using DAB dyes. The tool requires as input the original RGB images, and the FastRed, FastBlue, DAB channel, easily obtained using the Fiji function: "ImageJ" -> "Image" -> "Colour Deconvolution" -> "FastRed FastBlue DAB" Then, the tool first segment the nuclei using the FastBlue channel and the DAB channel, and then computes statistics by subdividing the sample in three regions according to the FastRed channel: a dark-red ROI, a light-pink ROI and a white ROI. ANDTool is written in MATLAB (The MathWorks, Inc., Massachusetts, USA) and the source code and standalone versions are here available for download. USER MANUAL: see the specific PDF available in the Files section. REQUIREMENTS: MATLAB R2017b and Image Processing Toolbox 10.1 or later versions. MAIN CONTACT: Filippo Piccinini (E-mail: filippo.piccinini85@gmail.com) -
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ARKit + CoreLocation
Combines the high accuracy of AR with the scale of GPS data
ARKit uses camera and motion data to map out the local world as you move around. CoreLocation uses wifi and GPS data to determine your global location, with a low degree of accuracy. ARKit + CoreLocation combines the high accuracy of AR with the scale of GPS data. The potential for combining these technologies is huge, with so many potential applications across many different areas. Allow items to be placed within the AR world using real-world coordinates. Dramatically improved location accuracy, using recent location data points combined with knowledge about movement through the AR world. The improved location accuracy is currently in an “experimental” phase, but could be the most important component. The library and demo come with a bunch of additional features for configuration. It’s all fully documented to be sure to have a look around. -
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Adaptative Backgrounds
A jQuery plugin for extracting the dominant color from images
A jQuery plugin for extracting dominant colors from images and applying it to its parent. Install via bower. Then simply include jQuery and the script in your page, and invoke it like so. Instead of using an <img> element nested inside of parent element, AB supports grabbing the dominant color of a background image of a standalone element, then applying the corresponding dominant color as the background color of said element. Enable this functionality by adding a data property, data-ab-css-background to the element. selector String (default: 'img[data-adaptive-background="1"]') a CSS selector which denotes which images to grab/process. Ideally, this selector would start with img, to ensure we only grab and try to process actual images. parent falsy (default: null) a CSS selector which denotes which parent to apply the background color to. By default, the color is applied to the parent one level up the DOM tree. -
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AiHound
AI powered image classification for nudity and documents / id-cards
AI Hound is designed to run from an USB pendrive or any other kind of removeable and writeable media. The programm checks all Office-documents, Images and videos for various categories for images. Actually It can recognice nudity/porn and scanned or photographed documents / ID- and credit-cards. I am working on a model that also recognice various types of drugs in images. -
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CSSgram
CSS library for Instagram filters
Simply put, CSSgram is a library for editing your images with Instagram-like filters directly using CSS. What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a content-block like <img>. The recommendation is to wrap your images in a <figure> tag. If you use custom naming in your CSS architecture, you can add the .scss files for the provided styles within your project and then @extend the filter effects within your style definitions. Mixins allow for multiple filter arguments to be passed into your classes. This is useful for if you want to add filters in addition to the ones provided (i.e. add a blur).
