What each tool is actually for, described plainly. No ratings, no scores, no invented benchmarks.
Vision and OCR: Reading images rather than making them: text extraction, document parsing, segmentation and object detection.
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
Written mainly in Python. Released under the Apache-2.0 licence.
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
Written mainly in Jupyter Notebook. Released under the Apache-2.0 licence.
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
Written mainly in Python. Released under the Apache-2.0 licence.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Written mainly in Jupyter Notebook. Released under the Apache-2.0 licence.
We write your reusable computer vision tools. 💜
Written mainly in Python. Released under the MIT licence.
OCR, layout analysis, reading order, table recognition in 90+ languages
Written mainly in Python. Released under the Apache-2.0 licence.
Tesseract Open Source OCR Engine (main repository)
Written mainly in C++. Released under the Apache-2.0 licence.
State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
Written mainly in JavaScript. Released under the Apache-2.0 licence.
Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
Written mainly in Python. Released under the AGPL-3.0 licence.