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MindOCR

Introduction

MindOCR is an open-source toolbox for OCR development and application based on MindSpore, which integrates series of mainstream text detection and recognition algorihtms/models, provides easy-to-use training and inference tools. It can accelerate the process of developing and deploying SoTA text detection and recognition models in real-world applications, such as DBNet/DBNet++ and CRNN/SVTR, and help fulfill the need of image-text understanding.

Major Features
  • Modular design: We decoupled the OCR task into several configurable modules. Users can setup the training and evaluation pipelines, customize the data processing pipeline and model architectures easily by modifying just few lines of code.
  • High-performance: MindOCR provides a series of pretrained weights trained with optimized configurations that reach competitive performance on OCR tasks.
  • Low-cost-to-apply: Easy-to-use inference tools are provided in MindOCR to perform text detection and recognition tasks.

The following is the corresponding mindocr versions and supported mindspore versions.

mindocr mindspore
main master
0.4 2.3.0/2.3.1
0.3 2.2.10
0.1 1.8

Installation

Details

Prerequisites

MindOCR is built on MindSpore AI framework and is compatible with the following framework versions. installation guideline for Training, please refer to the installation links shown below.

  • mindspore [install] Please install correct MindSpore version refer to mindocr versions.
  • python >= 3.7
  • openmpi 4.0.3 (for distributed training/evaluation) [install]

MindSpore Lite offline Inference please refer to Lite offline Environment Installation

Dependency

pip install -r requirements.txt

Install from Source (recommend)

git clone https://github.com/mindspore-lab/mindocr.git
cd mindocr
pip install -e .

Using -e for "editable" mode can help resolve potential module import issues.

Install from docker

Details

The environment information of dockers provided is as following:

  • OS：Euler2.8
  • CANN：7.0
  • Python：3.9
  • MindSpore：2.2.10
  • MindSpore Lite：2.2.10

Please follow the steps to install docker：

  1. Download docker

    • 910： 
      docker pull swr.cn-central-221.ovaijisuan.com/mindocr/mindocr_dev_910_ms_2_2_10_cann7_0_py39:v1
    • 910*: 
      docker pull swr.cn-central-221.ovaijisuan.com/mindocr/mindocr_dev_ms_2_2_10_cann7_0_py39:v1

  2. Create container

    docker_name="temp_mindocr"
# 910
image_name="swr.cn-central-221.ovaijisuan.com/mindocr/mindocr_dev_910_ms_2_2_10_cann7_0_py39:v1"
# 910*
image_name="swr.cn-central-221.ovaijisuan.com/mindocr/mindocr_dev_ms_2_2_10_cann7_0_py39:v1"

docker run --privileged --name ${docker_name} \
    --tmpfs /tmp \
    --tmpfs /run \
    -v /sys/fs/cgroup:/sys/fs/cgroup:ro \
    --device=/dev/davinci1 \
    --device=/dev/davinci2 \
    --device=/dev/davinci3 \
    --device=/dev/davinci4 \
    --device=/dev/davinci5 \
    --device=/dev/davinci6 \
    --device=/dev/davinci7 \
    --device=/dev/davinci_manager \
    --device=/dev/hisi_hdc \
    --device=/dev/devmm_svm \
    -v /etc/localtime:/etc/localtime \
    -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    --shm-size 800g \
    --cpus 96 \
    --security-opt seccomp=unconfined \
    --network=bridge -itd ${image_name} bash

  3. Enter container

    # set docker id
container_id="your docker id"
docker exec -it --user root $container_id bash

  4. Set environment variables After entering container, set environment variables by the following command：

    source env_setup.sh

Install from PyPI

pip install mindocr

As this project is under active development, the version installed from PyPI is out-of-date currently. (will update soon).

Quick Start

1. Text Detection and Recognition Demo

After installing MindOCR, we can run text detection and recognition on an arbitrary image easily as follows.

python tools/infer/text/predict_system.py --image_dir {path_to_img or dir_to_imgs} \
                                          --det_algorithm DB++  \
                                          --rec_algorithm CRNN  \
                                          --visualize_output True

After running, the results will be saved in ./inference_results by default. Here is an example result.

Visualization of text detection and recognition result

We can see that all texts on the image are detected and recognized accurately. For more usage, please refer to the inference section in tutorials.

2. Model Training and Evaluation - Quick Guideline

It is easy to train your OCR model with the tools/train.py script, which supports both text detection and recognition model training.

python tools/train.py --config {path/to/model_config.yaml}

The --config arg specifies the path to a yaml file that defines the model to be trained and the training strategy including data process pipeline, optimizer, lr scheduler, etc.

MindOCR provides SoTA OCR models with their training strategies in configs folder. You may adapt it to your task/dataset, for example, by running

# train text detection model DBNet++ on icdar15 dataset
python tools/train.py --config configs/det/dbnet/dbpp_r50_icdar15.yaml
# train text recognition model CRNN on icdar15 dataset
python tools/train.py --config configs/rec/crnn/crnn_icdar15.yaml

Similarly, it is easy to evaluate the trained model with the tools/eval.py script.

python tools/eval.py \
    --config {path/to/model_config.yaml} \
    --opt eval.dataset_root={path/to/your_dataset} eval.ckpt_load_path={path/to/ckpt_file}

For more illustration and usage, please refer to the model training section in Tutorials.

3. Model Offline Inference

You can do MindSpore Lite inference in MindOCR using MindOCR models or Third-party models (PaddleOCR, MMOCR, etc.). Please refer to Model Offline Inference Tutorial

Tutorials

Model List

Text Detection
Text Recognition
Layout Analysis
Key Information Extraction
Table Recognition
OCR large model

For the detailed performance of the trained models, please refer to https://github.com/mindspore-lab/mindocr/blob/main/configs.

For details of MindSpore Lite inference models support, please refer to MindOCR Models Support List and Third-party Models Support List (PaddleOCR etc.).

Dataset List

MindOCR provides a dataset conversion tool to OCR datasets with different formats and support customized dataset by users. We have validated the following public OCR datasets in model training/evaluation.

General OCR Datasets
Layout Analysis Datasets
Key Information Extraction Datasets
Table Recognition Datasets

We will include more datasets for training and evaluation. This list will be continuously updated.

Frequently Asked Questions

Frequently asked questions about configuring environment and mindocr, please refer to FAQ.

Notes

What is New

News
  • 2023/04/01
  1. Add new trained models
  • 2024/03/20
  1. Add new trained models
    • Vary-toy for OCR large model, providing Qwen-1.8B LLM-based object detection and OCR abilities
  • 2023/12/25
  1. Add new trained models
  2. Add more benchmark datasets and their results
  • 2023/12/14
  1. Add new trained models
  2. Add more benchmark datasets and their results
  3. Multiple specifications support for Ascend 910: DBNet ResNet-50, DBNet++ ResNet-50, CRNN VGG7, SVTR-Tiny, FCENet, ABINet
  • 2023/11/28
  1. Add offline inference support for PP-OCRv4
  2. Fix bugs of third-party models offline inference
  • 2023/11/17
  1. Add new trained models
  2. Add more benchmark datasets and their results
  • 2023/07/06
  1. Add new trained models
  • 2023/07/05
  1. Add new trained models
  • 2023/06/29
  1. Add new trained models
  • 2023/06/07
  1. Add new trained models
    • PSENet for text detection
    • EAST for text detection
    • SVTR for text recognition
  2. Add more benchmark datasets and their results
  3. Add resume training function, which can be used in case of unexpected interruption in training. Usage: add the resume parameter under the model field in the yaml config, e.g.,resume: True, load and resume training from {ckpt_save_dir}/train_resume.ckpt or resume: /path/to/train_resume.ckpt, load and resume training from the given path.
  4. Improve postprocessing for detection: re-scale detected text polygons to original image space by default, which can be enabled by add "shape_list" to the eval.dataset.output_columns list.
  5. Refactor online inference to support more models, see README.md for details.
  • 2023/05/15
  1. Add new trained models
  2. Add more benchmark datasets and their results
  3. Add checkpoint manager for saving top-k checkpoints and improve log.
  4. Python inference code refactored.
  5. Bug fix: use Meter to average loss for large datasets, disable pred_cast_fp32 for ctcloss in AMP training, fix error when invalid polygons exist.
  • 2023/05/04
  1. Support loading self-defined pretrained checkpoints via setting model-pretrained with checkpoint url or local path in yaml.
  2. Support setting probability for executing augmentation including rotation and flip.
  3. Add Exponential Moving Average(EMA) for model training, which can be enabled by setting train-ema (default: False) and train-ema_decay in the yaml config.
  4. Arg parameter changed：num_columns_to_net -> net_input_column_index: change the column number feeding into the network to the column index.
  5. Arg parameter changed：num_columns_of_labels -> label_column_index: change the column number corresponds to the label to the column index.
  • 2023/04/21
  1. Add parameter grouping to support flexible regularization in training. Usage: add grouping_strategy argument in yaml config to select a predefined grouping strategy, or use no_weight_decay_params argument to pick layers to exclude from weight decay (e.g., bias, norm). Example can be referred in configs/rec/crnn/crnn_icdar15.yaml
  2. Add gradient accumulation to support large batch size training. Usage: add gradient_accumulation_steps in yaml config, the global batch size = batch_size * devices * gradient_accumulation_steps. Example can be referred in configs/rec/crnn/crnn_icdar15.yaml
  3. Add gradient clip to support training stablization. Enable it by setting grad_clip as True in yaml config.
  • 2023/03/23
  1. Add dynamic loss scaler support, compatible with drop overflow update. To enable dynamic loss scaler, please set type of loss_scale as dynamic. A YAML example can be viewed in configs/rec/crnn/crnn_icdar15.yaml
  • 2023/03/20
  1. Arg names changed: output_keys -> output_columns, num_keys_to_net -> num_columns_to_net
  2. Data pipeline updated
  • 2023/03/13
  1. Add system test and CI workflow.
  2. Add modelarts adapter to allow training on OpenI platform. To train on OpenI: 
        i)   Create a new training task on the openi cloud platform.
    ii)  Link the dataset (e.g., ic15_mindocr) on the webpage.
    iii) Add run parameter `config` and write the yaml file path on the website UI interface, e.g., '/home/work/user-job-dir/V0001/configs/rec/test.yaml'
    iv)  Add run parameter `enable_modelarts` and set True on the website UI interface.
    v)   Fill in other blanks and launch.

How to Contribute

We appreciate all kinds of contributions including issues and PRs to make MindOCR better.

Please refer to CONTRIBUTING.md for the contributing guideline. Please follow the Model Template and Guideline for contributing a model that fits the overall interface :)

License

This project follows the Apache License 2.0 open-source license.

Citation

If you find this project useful in your research, please consider citing:

@misc{MindSpore OCR 2023,
    title={{MindSpore OCR }:MindSpore OCR Toolbox},
    author={MindSpore Team},
    howpublished = {\url{https://github.com/mindspore-lab/mindocr/}},
    year={2023}
}