Open Source C++ Image Recognition Software

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.

PaddleOCR offers exceptional, multilingual, and practical Optical Character Recognition (OCR) tools that can help users train better models and apply them into practice. Inspired by PaddlePaddle, PaddleOCR is an ultra lightweight OCR system, with multilingual recognition, digit recognition, vertical text recognition, as well as long text recognition. It features a PPOCR series of high-quality pre-trained models, which includes: ultra lightweight ppocr_mobile series models, general ppocr_server series models, and ultra lightweight compression ppocr_mobile_slim series models. PaddleOCR is easy to install and easy to use on Windows, Linux, MacOS and other systems.

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.

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.

AI File Sorter is a cross-platform desktop application that uses AI to organize all files and suggest better file names for image files, based on their visual content. The app can analyze picture files locally and suggest meaningful, human-readable names. For example, a generic file like IMG_2048.jpg can be renamed to clouds_over_lake.jpg. All rename and categorization suggestions are optional and must be reviewed and approved before anything is applied. AI File Sorter helps tidy up cluttered folders such as Downloads, external drives, or NAS storage by grouping files based on filenames, extensions, folder context, and learned organization patterns. The application can run fully offline using local AI models such as Mistral 7B or LLaMa 3B. When using local models, all processing happens entirely on your device — no files, images, or metadata are uploaded, and no telemetry is sent.

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).

This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C source files. The source code does not depend on any other libraries. What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler. SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM. The model file has also been provided in directory ./models/. The file examples/detect-image.cpp and examples/detect-camera.cpp show how to use the library. The library was trained by libfacedetection.train. You can copy the files in directory src/ into your project, and compile them as the other files in your project. The source code is written in standard C/C++. It should be compiled at any platform which supports C/C++.