Deep Text Recognition Benchmark Project Overview
Deep Text Recognition Benchmark is a comprehensive framework focusing on scene text recognition (STR). This project is designed to provide a consistent and reliable platform to compare different STR models by analyzing datasets and models themselves. It aims to enhance understanding of the performance gains achieved by existing modules through standardized datasets and methodologies.
Project Contributions
The official PyTorch implementation of this benchmark follows a four-stage framework widely applicable to most STR models. This structured approach helps evaluate and improve performance as it measures accuracy, speed, and memory usage coherently.
Competitive Edge
Utilizing this framework, the project excelled in numerous text recognition challenges: it clinched first place in the ICDAR2013 focused scene text and ICDAR2019 ArT competitions, and achieved third place in the ICDAR2017 COCO-Text and ICDAR2019 ReCTS tasks.
Project Updates
Since its inception, the project has undergone several updates:
- August 3, 2020: Introduction of Baidu warpctc guidelines to reproduce CTC results.
- December 27, 2019: Inclusion of FLOPS in their analysis and minor updates.
- October 22, 2019: Added confidence scores and improved training log outputs.
- July 31, 2019: Recognition at the International Conference on Computer Vision (ICCV) 2019.
Getting Started
The benchmark uses PyTorch and requires several dependencies such as lmdb, Pillow, torchvision, nltk, and natsort. The process begins with downloading the necessary datasets and pretrained models, which are crucial for training and evaluating models efficiently.
Running Demonstrative Models
To run demos with the TRBA (TPS-ResNet-BiLSTM-Attn) model:
- Download the pretrained model.
- Add images for testing to the
demo_image/folder. - Execute with the prescribed command using PyTorch.
Training and Evaluation
The project provides a straightforward guide to train and evaluate models like CRNN and TRBA using the benchmark datasets. Key parameters such as feature extraction methods, sequence modeling, and prediction techniques can be adjusted to experiment with different configurations.
Expanding Capabilities
For users needing custom datasets or working with Non-Latin languages, the project supports creating personalized lmdb datasets. Adjusting data selection and ratios to optimize performance on specific datasets is also supported.
Acknowledgements and References
The Deep Text Recognition Benchmark acknowledges contributions from established repositories like crnn.pytorch and ocr_attention, ensuring a robust foundation upon which the project builds.
Licensing and Contact
The project is released under the Apache License 2.0. Users are encouraged to cite this work in their publications, reflecting its contributions to the STR domain.
The project team remains open for collaboration and provides contact points for code-related inquiries and broader collaboration interests.
With its structured approach and comprehensive resources, the Deep Text Recognition Benchmark is an invaluable tool for researchers and practitioners aiming to achieve excellence in scene text recognition.