Introduction to MMOCR
MMOCR is an innovative open-source toolbox developed by the OpenMMLab project. It specializes in tasks related to optical character recognition (OCR), including text detection, text recognition, and key information extraction. This powerful toolbox is built upon PyTorch and mmdetection, incorporating a range of state-of-the-art models and utilities to facilitate OCR tasks and their downstream applications.
Major Features
Comprehensive Pipeline
MMOCR supports a complete pipeline for text detection and recognition, along with downstream tasks such as key information extraction. This comprehensive capability makes it a go-to solution for many OCR needs.
Multiple Models
The toolbox offers a variety of cutting-edge models specifically designed for text detection, recognition, and key information extraction. This enables users to select the best-suited models for their specific requirements.
Modular Design
With a modular architecture, MMOCR allows users to customize different components of the models. Users can define their optimizers, data preprocessors, and model parts such as backbones, necks, and heads. This flexibility is invaluable for creating tailored models to meet unique project needs.
Numerous Utilities
MMOCR comes equipped with a complete set of utilities for evaluating and enhancing model performance. Users can visualize images, ground truths, and predictions, as well as utilize validation tools and data converters to support their projects.
Installation
To utilize MMOCR, there are a few dependencies required, such as PyTorch, MMEngine, MMCV, and MMDetection. Here's a quick way to get started with installation:
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
git clone https://github.com/open-mmlab/mmocr.git
cd mmocr
mim install -e .
For detailed instructions, refer to the installation guide.
Getting Started
MMOCR provides an easy-to-follow quick run guide for beginners eager to explore its capabilities. This guide walks users through the basic usage of the toolbox, helping them to quickly integrate OCR functionalities into their projects.
Supported Models
MMOCR supports a wide array of models spread across various OCR tasks:
- Text Detection Models: Includes DBNet, DBNet++, Mask R-CNN, PANet, PSENet, TextSnake, DRRG, and FCENet.
- Text Recognition Models: Features ABINet, ASTER, CRNN, MASTER, NRTR, RobustScanner, SAR, SATRN, and SVTR.
- Key Information Extraction Models: Offers SDMG-R.
- Text Spotting Models: Includes ABCNet, ABCNetV2, and SPTS.
For a comprehensive look at these models, users can refer to the model zoo.
Community and Contributions
MMOCR is a community-driven project with contributions from researchers and developers globally. The project welcomes enhancements and feedback from users. To contribute, users can follow guidelines available in the contributing section.
Conclusion
MMOCR stands out as a versatile and comprehensive toolkit for achieving high-performance OCR solutions. By combining powerful models with a modular and user-friendly design, it caters to a broad spectrum of OCR needs. Whether for research or commercial applications, MMOCR aids in efficiently implementing and creating OCR methods that push the boundaries of text detection and recognition technology.