Introduction | News | Installation | Weights | Citation | Acknowledgement
The ICDAR Robust Reading Competition is internationally recognized as an authoritative event in the field of text recognition. The data evaluation and metrics in top conference papers in the text recognition field often come from ICDAR competition data and metrics. Generally, there are several major events each year, and each event is further divided into 3-4 competitions.
Text on digitized historical maps contains valuable information providing georeferenced political and cultural context, yet the wealth of information in digitized historical maps remains largely inaccessible due to their unsearchable raster format. The ICDAR24 MapText Competition aims to address the unique challenges of detecting and recognizing textual information (e.g., place names) and linking words to form location phrases on historical maps.
This project is one of the current leaders in the MapText competition.
2024.07.28 Repo forked from main repo. This fork may not reflect changes in the main repo from this point.
git clone https://github.com/maps-as-data/MapTextPipeline.git
cd MapTextPipeline
pip install -v .
Fine-tuned model weights can be downloaded from: https://drive.google.com/file/d/1Okvl5tlWusJxDCdDv_CLsGKQ5elImfx4/view?usp=drive_link
This project utilizes methods related to DNTextSpotter. If you find MapTextPipeline helpful, please consider giving this repo a star ⭐ and citing:
@article{xie2024dntextspotter,
title={DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training},
author={Xie, Yu and Qiao, Qian and Gao, Jun and Wu, Tianxiang and Huang, Shaoyao and Fan, Jiaqing and Cao, Ziqiang and Wang, Zili and Zhang, Yue and Zhang, Jielei and others},
journal={arXiv preprint arXiv:2408.00355},
year={2024}
}
This project is based on Adelaidet and DeepSolo. For academic use, this project is licensed under the 2-clause BSD License.