Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

Related tags

Deep LearningABINet
Overview

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

The official code of ABINet (CVPR 2021, Oral).

ABINet uses a vision model and an explicit language model to recognize text in the wild, which are trained in end-to-end way. The language model (BCN) achieves bidirectional language representation in simulating cloze test, additionally utilizing iterative correction strategy.

framework

Runtime Environment

  • We provide a pre-built docker image using the Dockerfile from docker/Dockerfile

  • Running in Docker

    $ [email protected]:FangShancheng/ABINet.git
    $ docker run --gpus all --rm -ti --ipc=host -v $(pwd)/ABINet:/app fangshancheng/fastai:torch1.1 /bin/bash
    
  • (Untested) Or using the dependencies

    pip install -r requirements.txt
    

Datasets

  • Training datasets

    1. MJSynth (MJ):
    2. SynthText (ST):
    3. WikiText103, which is only used for pre-trainig language models:
  • Evaluation datasets, LMDB datasets can be downloaded from BaiduNetdisk(passwd:1dbv), GoogleDrive.

    1. ICDAR 2013 (IC13)
    2. ICDAR 2015 (IC15)
    3. IIIT5K Words (IIIT)
    4. Street View Text (SVT)
    5. Street View Text-Perspective (SVTP)
    6. CUTE80 (CUTE)
  • The structure of data directory is

    data
    ├── charset_36.txt
    ├── evaluation
    │   ├── CUTE80
    │   ├── IC13_857
    │   ├── IC15_1811
    │   ├── IIIT5k_3000
    │   ├── SVT
    │   └── SVTP
    ├── training
    │   ├── MJ
    │   │   ├── MJ_test
    │   │   ├── MJ_train
    │   │   └── MJ_valid
    │   └── ST
    ├── WikiText-103.csv
    └── WikiText-103_eval_d1.csv
    

Pretrained Models

Get the pretrained models from BaiduNetdisk(passwd:kwck), GoogleDrive. Performances of the pretrained models are summaried as follows:

Model IC13 SVT IIIT IC15 SVTP CUTE AVG
ABINet-SV 97.1 92.7 95.2 84.0 86.7 88.5 91.4
ABINet-LV 97.0 93.4 96.4 85.9 89.5 89.2 92.7

Training

  1. Pre-train vision model
    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_vision_model.yaml
    
  2. Pre-train language model
    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_language_model.yaml
    
  3. Train ABINet
    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/train_abinet.yaml
    

Note:

  • You can set the checkpoint path for vision and language models separately for specific pretrained model, or set to None to train from scratch

Evaluation

CUDA_VISIBLE_DEVICES=0 python main.py --config=configs/train_abinet.yaml --phase test --image_only

Additional flags:

  • --checkpoint /path/to/checkpoint set the path of evaluation model
  • --test_root /path/to/dataset set the path of evaluation dataset
  • --model_eval [alignment|vision] which sub-model to evaluate
  • --image_only disable dumping visualization of attention masks

Visualization

Successful and failure cases on low-quality images:

cases

Citation

If you find our method useful for your reserach, please cite

@article{fang2021read,
  title={Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
  author={Fang, Shancheng and Xie, Hongtao and Wang, Yuxin and Mao, Zhendong and Zhang, Yongdong},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

License

This project is only free for academic research purposes, licensed under the 2-clause BSD License - see the LICENSE file for details.

Feel free to contact [email protected] if you have any questions.

Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]

Face Identity Disentanglement via Latent Space Mapping Description Official Implementation of the paper Face Identity Disentanglement via Latent Space

150 Dec 07, 2022
1st Solution For NeurIPS 2021 Competition on ML4CO Dual Task

KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a

MEGVII Research 24 Sep 08, 2022
ICLR 2021, Fair Mixup: Fairness via Interpolation

Fair Mixup: Fairness via Interpolation Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predicti

Ching-Yao Chuang 49 Nov 22, 2022
LoL Runes Recommender With Python

LoL-Runes-Recommender Para ejecutar la aplicación se debe llamar a execute_app.p

Sebastián Salinas 1 Jan 10, 2022
Pipeline for employing a Lightweight deep learning models for LOW-power systems

PL-LOW A high-performance deep learning model lightweight pipeline that gradually lightens deep neural networks in order to utilize high-performance d

POSTECH Data Intelligence Lab 9 Aug 13, 2022
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Continuous Query Decomposition This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neura

UCL Natural Language Processing 71 Dec 29, 2022
Deep Two-View Structure-from-Motion Revisited

Deep Two-View Structure-from-Motion Revisited This repository provides the code for our CVPR 2021 paper Deep Two-View Structure-from-Motion Revisited.

Jianyuan Wang 145 Jan 06, 2023
LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and

TuZheng 405 Jan 04, 2023
ImageNet Adversarial Image Evaluation

ImageNet Adversarial Image Evaluation This repository contains the code and some materials used in the experimental work presented in the following pa

Utku Ozbulak 11 Dec 26, 2022
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

CSAW-M This repository contains code for CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer. Source code for tr

Yue Liu 7 Oct 11, 2022
An excellent hash algorithm combining classical sponge structure and RNN.

SHA-RNN Recurrent Neural Network with Chaotic System for Hash Functions Anonymous Authors [摘要] 在这次作业中我们提出了一种新的 Hash Function —— SHA-RNN。其以海绵结构为基础,融合了混

Houde Qian 5 May 15, 2022
Deep Sketch-guided Cartoon Video Inbetweening

Cartoon Video Inbetweening Paper | DOI | Video The source code of Deep Sketch-guided Cartoon Video Inbetweening by Xiaoyu Li, Bo Zhang, Jing Liao, Ped

Xiaoyu Li 37 Dec 22, 2022
Implementation of GGB color space

GGB Color Space This package is implementation of GGB color space from Development of a Robust Algorithm for Detection of Nuclei and Classification of

Resha Dwika Hefni Al-Fahsi 2 Oct 06, 2021
Optimizing synthesizer parameters using gradient approximation

Optimizing synthesizer parameters using gradient approximation NASH 2021 Hackathon! These are some experiments I conducted during NASH 2021, the Neura

Jordie Shier 10 Feb 10, 2022
Automated Evidence Collection for Fake News Detection

Automated Evidence Collection for Fake News Detection This is the code repo for the Automated Evidence Collection for Fake News Detection paper accept

Mrinal Rawat 2 Apr 12, 2022
Trading Strategies for Freqtrade

Freqtrade Strategies Strategies for Freqtrade, developed primarily in a partnership between @werkkrew and @JimmyNixx from the Freqtrade Discord. Use t

Bryan Chain 242 Jan 07, 2023
LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

Query Selector Here you can find code and data loaders for the paper https://arxiv.org/pdf/2107.08687v1.pdf . Query Selector is a novel approach to sp

MORAI 62 Dec 17, 2022
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
Its a Plant Leaf Disease Detection System based on Machine Learning.

My_Project_Code Its a Plant Leaf Disease Detection System based on Machine Learning. I have used Tomato Leaves Dataset from kaggle. This system detect

Sanskriti Sidola 3 Jun 15, 2022
Official repository for the CVPR 2021 paper "Learning Feature Aggregation for Deep 3D Morphable Models"

Deep3DMM Official repository for the CVPR 2021 paper Learning Feature Aggregation for Deep 3D Morphable Models. Requirements This code is tested on Py

38 Dec 27, 2022