What is mmocr?

MMOCR is a Python toolbox for reading and understanding text in images. It handles three related tasks: finding where text appears in an image (text detection), reading what the text says (text recognition), and pulling out structured information from documents like receipts or forms (key information extraction). The project is part of OpenMMLab, a research organization that builds open-source tools for computer vision tasks. The toolbox is built on top of PyTorch, which is a machine learning framework, along with several other OpenMMLab libraries. Because it depends on these specific frameworks, it is aimed at researchers and developers who already work in that ecosystem. Installation involves setting up a conda environment, installing the dependencies in a specific order, and then installing MMOCR itself. One of the main appeals of the toolbox is the range of published algorithms it includes. For text detection, it supports methods like DBNet, PSENet, PANet, TextSnake, and several others, many of which were published at major academic conferences. For text recognition, it includes CRNN, ABINet, SATRN, and more. These are research models that developers can use directly or adapt for their own data. A model zoo page in the documentation lists all supported algorithms with links to the original papers. The design is modular, meaning individual pieces such as the data loader, the model backbone, and the training loss function can be swapped out or replaced. This makes it easier to run experiments comparing different approaches without rewriting everything from scratch. Utility tools are included for visualizing what the model detects and for converting datasets into the format the toolbox expects. Version 1.0.0 was released in April 2023. Anyone coming from the older 0.6.3 version needs to follow a migration guide because the internal structure changed significantly. Documentation, tutorials, and a Jupyter notebook for getting started are all linked from the README.