GeneralOCR is open source Optical Character Recognition based on PyTorch.

Overview

Introduction

GeneralOCR is open source Optical Character Recognition based on PyTorch. It makes a fidelity and useful tool to implement SOTA models on OCR domain. You can use them to infer and train the model with your customized dataset. The solution architecture of this project is re-implemented from facebook Detectron and openmm-cv.

Installation

Refer to the guideline of gen_ocr installation

Inference

Configuration

Model text detection

Supported Algorithms:

Text Detection
Algorithm Paper Python argument (--det)
- [x] DBNet (AAAI'2020) https://arxiv.org/pdf/1911.08947 DB_r18, DB_r50
- [x] Mask R-CNN (ICCV'2017) https://arxiv.org/abs/1703.06870 MaskRCNN_CTW, MaskRCNN_IC15, MaskRCNN_IC17
- [x] PANet (ICCV'2019) https://arxiv.org/abs/1908.06391 PANet_CTW, PANet_IC15
- [x] PSENet (CVPR'2019) https://arxiv.org/abs/1903.12473 PS_CTW, PS_IC15
- [x] TextSnake (ECCV'2018) https://arxiv.org/abs/1807.01544 TextSnake
- [x] DRRG (CVPR'2020) https://arxiv.org/abs/2003.07493 DRRG
- [x] FCENet (CVPR'2021) https://arxiv.org/abs/2104.10442 FCE_IC15, FCE_CTW_DCNv2

Table 1: Text detection algorithms, papers and arguments configuration in package.

Model text recognition

Text Recognition
Algorithm Paper Python argument (--recog)
- [x] CRNN (TPAMI'2016) https://arxiv.org/abs/1507.05717 CRNN, CRNN_TPS
- [x] NRTR (ICDAR'2019) https://arxiv.org/abs/1806.00926 NRTR_1/8-1/4, NRTR_1/16-1/8
- [x] RobustScanner (ECCV'2020) https://arxiv.org/abs/2007.07542 RobustScanner
- [x] SAR (AAAI'2019) https://arxiv.org/abs/1811.00751 SAR
- [x] SATRN (CVPR'2020 Workshop on Text and Documents in the Deep Learning Era) https://arxiv.org/abs/1910.04396 SATRN, SATRN_sm
- [x] SegOCR (Manuscript'2021) - SEG

Table 2: Text recognition algorithms, papers and arguments configuration in package.

Inference

# Activate your conda environment
conda activate gen_ocr
python general_ocr/utils/ocr.py demo/demo_text_ocr_2.jpg --print-result --imshow --det TextSnake --recog SEG

--det and --recog argument values are supplied in table 1 and table 2.

The result as below:

demo image 1

Training

Training with toy dataset

We prepare toy datasets for you to train on /tests/data folder in which you can do your experiment before training with the official datasets.

python tools/train.py configs/textrecog/robust_scanner/seg_r31_1by16_fpnocr_toy_dataset.py --work-dir seg

To change text recognition algorithm into sag:

python tools/train.py configs/textrecog/sar/sar_r31_parallel_decoder_toy_dataset.py --work-dir sar

Training with Academic dataset

When you train Academic dataset, you need to setup dataset directory as this guideline. The main point you should forecus is that your model point to the right dataset directory. Assume that you want to train model TextSnake on CTW1500 dataset, thus your config file of that model in configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py should be as below:

dataset_type = 'IcdarDataset'
data_root = 'data/ctw1500/'


data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    val_dataloader=dict(samples_per_gpu=1),
    test_dataloader=dict(samples_per_gpu=1),
    train=dict(
        type=dataset_type,
        ann_file=f'{data_root}/instances_training.json',
        img_prefix=f'{data_root}/imgs',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=f'{data_root}/instances_test.json',
        img_prefix=f'{data_root}/imgs',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=f'{data_root}/instances_test.json',
        img_prefix=f'{data_root}/imgs',
        pipeline=test_pipeline))

Your data_root folder data/ctw1500/ have to be right. Afterward, train your model:

python tools/train.py configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py --work-dir textsnake

To study other configuration parameters on training.

Testing

Now you completed training of TextSnake and get the checkpoint textsnake/lastest.pth. You should evaluate peformance on test set using hmean-iou metric:

python tools/test.py configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py textsnake/latest.pth --eval hmean-iou

Citation

If you find this project is useful in your reasearch, kindly consider cite:

@article{genearal_ocr,
    title={GeneralOCR:  A Comprehensive package for OCR models},
    author={khanhphamdinh},
    email= {[email protected]},
    year={2021}
}
You might also like...
 a reimplementation of Optical Flow Estimation using a Spatial Pyramid Network in PyTorch
a reimplementation of Optical Flow Estimation using a Spatial Pyramid Network in PyTorch

pytorch-spynet This is a personal reimplementation of SPyNet [1] using PyTorch. Should you be making use of this work, please cite the paper according

 OpenGAN: Open-Set Recognition via Open Data Generation
OpenGAN: Open-Set Recognition via Open Data Generation

OpenGAN: Open-Set Recognition via Open Data Generation ICCV 2021 (oral) Real-world machine learning systems need to analyze novel testing data that di

Face Library is an open source package for accurate and real-time face detection and recognition
Face Library is an open source package for accurate and real-time face detection and recognition

Face Library Face Library is an open source package for accurate and real-time face detection and recognition. The package is built over OpenCV and us

CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

Character Controllers using Motion VAEs

Character Controllers using Motion VAEs This repo is the codebase for the SIGGRAPH 2020 paper with the title above. Please find the paper and demo at

An addon uses SMPL's poses and global translation to drive cartoon character in Blender.
An addon uses SMPL's poses and global translation to drive cartoon character in Blender.

Blender addon for driving character The addon drives the cartoon character by passing SMPL's poses and global translation into model's armature in Ble

a reccurrent neural netowrk that when trained on a peice of text and fed a starting prompt will write its on 250 character text using LSTM layers

RNN-Playwrite a reccurrent neural netowrk that when trained on a peice of text and fed a starting prompt will write its on 250 character text using LS

Scripts and a shader to get you started on setting up an exported Koikatsu character in Blender.
Scripts and a shader to get you started on setting up an exported Koikatsu character in Blender.

KK Blender Shader Pack A plugin and a shader to get you started with setting up an exported Koikatsu character in Blender. The plugin is a Blender add

Character-Input - Create a program that asks the user to enter their name and their age

Character-Input Create a program that asks the user to enter their name and thei

Comments
  • Please consider License seriously

    Please consider License seriously

    I found that your repository is based on the mmocr repo of OpenMMLab (https://github.com/open-mmlab/mmocr). Please at least cite the repo and preserve the copyrights before redistribution to acknowledge the authors' works.

    Thanks.

    opened by VinhLoiIT 1
  • Import error: undefine symbol

    Import error: undefine symbol

    Dear author, When I run the test command: python general_ocr/utils/ocr.py demo/mrbean.png --print-result --imshow --det TextSnake --recog SEG

    The output error is like this: ImportError: /home/avlab/general_ocr/general_ocr/_ext.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _Z42SigmoidFocalLossBackwardCUDAKernelLauncherN2at6TensorES0_S0_S0_ff

    Do you know the problem and how to fix that, please?

    opened by theohsiung 0
  • ModuleNotFoundError: No module named 'general_ocr._ext'

    ModuleNotFoundError: No module named 'general_ocr._ext'

    Dear author, When I run the test command: python general_ocr/utils/ocr.py demo/mrbean.png --print-result --imshow --det TextSnake --recog SEG

    The output error is like this: ModuleNotFoundError: No module named 'general_ocr._ext', although I have installed the repo following the instruction in https://github.com/phamdinhkhanh/general_ocr/blob/main/docs/install.md.

    Do you know the problem and how to fix that, please?

    opened by ngthanhtin 3
  • ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.26' not found

    ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.26' not found

    Setup:

    Screen Shot 2021-10-17 at 1 17 03 AM

    Log ERROR:

    Traceback (most recent call last):
      File "general_ocr/utils/ocr.py", line 7, in <module>
        import general_ocr
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/__init__.py", line 10, in <module>
        from .apis import *
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/apis/__init__.py", line 2, in <module>
        from .inference import init_detector, model_inference, inference_detector
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/apis/inference.py", line 10, in <module>
        from general_ocr.core import get_classes
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/core/__init__.py", line 4, in <module>
        from .bbox import *  # noqa: F401, F403
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/core/bbox/__init__.py", line 8, in <module>
        from .samplers import (BaseSampler, CombinedSampler,
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/core/bbox/samplers/__init__.py", line 10, in <module>
        from .score_hlr_sampler import ScoreHLRSampler
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/core/bbox/samplers/score_hlr_sampler.py", line 3, in <module>
        from general_ocr.ops import nms_match
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/ops/__init__.py", line 2, in <module>
        from .ball_query import ball_query
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/ops/ball_query.py", line 7, in <module>
        ext_module = ext_loader.load_ext('_ext', ['ball_query_forward'])
      File "/usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/utils/ext_loader.py", line 13, in load_ext
        ext = importlib.import_module('general_ocr.' + name)
      File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
        return _bootstrap._gcd_import(name[level:], package, level)
    ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.26' not found (required by /usr/local/lib/python3.7/dist-packages/general_ocr-0.0.1-py3.7.egg/general_ocr/_ext.cpython-37m-x86_64-linux-gnu.so)
    
    opened by Baristi000 1
Releases(general_ocr-0.0.1)
  • general_ocr-0.0.1(Oct 26, 2021)

    • Launch Project
    • Model support:
      • text detection: DBNet, Mask-RCNN, PANet, PSENet, TextSnake, DRRG, FCENet
      • text recognition: CRNN, NRTR, RobustScanner, SAR, SATRN, SegOCR
    Source code(tar.gz)
    Source code(zip)
Vit-ImageClassification - Pytorch ViT for Image classification on the CIFAR10 dataset

Vit-ImageClassification Introduction This project uses ViT to perform image clas

Kaicheng Yang 4 Jun 01, 2022
RGB-D Local Implicit Function for Depth Completion of Transparent Objects

RGB-D Local Implicit Function for Depth Completion of Transparent Objects [Project Page] [Paper] Overview This repository maintains the official imple

NVIDIA Research Projects 43 Dec 12, 2022
A library for building and serving multi-node distributed faiss indices.

About Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don't fit into a single server memory. It fol

Meta Research 170 Dec 30, 2022
ThunderSVM: A Fast SVM Library on GPUs and CPUs

What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss

Xtra Computing Group 1.4k Dec 22, 2022
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

Instance-Aware Latent-Space Search This is a PyTorch implementation of the following paper: Disentangled Face Attribute Editing via Instance-Aware Lat

67 Dec 21, 2022
A Simulation Environment to train Robots in Large Realistic Interactive Scenes

iGibson: A Simulation Environment to train Robots in Large Realistic Interactive Scenes iGibson is a simulation environment providing fast visual rend

Stanford Vision and Learning Lab 493 Jan 04, 2023
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Mayur 119 Nov 24, 2022
E2VID_ROS - E2VID_ROS: E2VID to a real-time system

E2VID_ROS Introduce We extend E2VID to a real-time system. Because Python ROS ca

Robin Shaun 7 Apr 17, 2022
MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

mediapipe-python-sample MediaPipeのPythonパッケージのサンプルです。 2020/12/11時点でPython実装のある以下4機能について用意しています。 Hands Pose Face Mesh Holistic Requirement mediapipe 0.

KazuhitoTakahashi 217 Dec 12, 2022
Continual reinforcement learning baselines: experiment specifications, implementation of existing methods, and common metrics. Easily extensible to new methods.

Continual Reinforcement Learning This repository provides a simple way to run continual reinforcement learning experiments in PyTorch, including evalu

55 Dec 24, 2022
Python Environment for Bayesian Learning

Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl in

Abhik Shah 103 Jul 14, 2022
🎯 A comprehensive gradient-free optimization framework written in Python

Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not

Devin Soni 565 Dec 26, 2022
Predicting Student Attentiveness using OpenCV

Predicting-Student-Attentiveness-using-OpenCV The model will predict if a student is attentive or not through facial parameter received through the st

Johann Pinto 2 Aug 20, 2022
Hierarchical Cross-modal Talking Face Generation with Dynamic Pixel-wise Loss (ATVGnet)

Hierarchical Cross-modal Talking Face Generation with Dynamic Pixel-wise Loss (ATVGnet) By Lele Chen , Ross K Maddox, Zhiyao Duan, Chenliang Xu. Unive

Lele Chen 218 Dec 27, 2022
Datasets for new state-of-the-art challenge in disentanglement learning

High resolution disentanglement datasets This repository contains the Falcor3D and Isaac3D datasets, which present a state-of-the-art challenge for co

NVIDIA Research Projects 37 May 26, 2022
[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

Discriminative Region-based Multi-Label Zero-Shot Learning (ICCV 2021) [arXiv][Project page coming soon] Sanath Narayan*, Akshita Gupta*, Salman Kh

Akshita Gupta 54 Nov 21, 2022
A PyTorch implementation of QANet.

QANet-pytorch NOTICE I'm very busy these months. I'll return to this repo in about 10 days. Introduction An implementation of QANet with PyTorch. Any

H. Z. 343 Nov 03, 2022
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

Memory-Efficient Multi-Level In-Situ Generation (MLG) By Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen and David Z. Pan

Jiaqi Gu 2 Jan 04, 2022
[CVPR 2022 Oral] MixFormer: End-to-End Tracking with Iterative Mixed Attention

MixFormer The official implementation of the CVPR 2022 paper MixFormer: End-to-End Tracking with Iterative Mixed Attention [Models and Raw results] (G

Multimedia Computing Group, Nanjing University 235 Jan 03, 2023
A keras implementation of ENet (abandoned for the foreseeable future)

ENet-keras This is an implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from ENet-training (lua-t

Pavlos 115 Nov 23, 2021