ICDAR https://rrc.cvc.uab.es/ This 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.
https://rrc.cvc.uab.es/?ch=28&com=introduction
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. This competition aims to address the unique challenges of detecting and recognizing textual information (e.g., place names) and linking words to form location phrases.
Python 3.8 + PyTorch 2.0.1 + CUDA 11.7 + Detectron2
conda create -n dnts python=3.8 -y
conda activate dnts
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
cd detectron2
pip install -e .
pip install -r requirements.txt
cd ..
python setup.py build develop
python tools/convert.py --input-json your_gt_path --output-json your_output_path --output_image_id_json your_output_image_id_path
python tools/convert_to_original.py --input-json your_pred_path --input_image_id_json your_input_image_id_path --output-json your_output_submition_format_path
You can download our pre-trained model in OneDrive and fine-tune it on the Rumsey dataset. The fine-tuning command is as follows:
python tools/train.py --config-file configs/ViTAEv2_S/rumsey/final_rumsey.yaml --num-gpus 2
You can also directly use our fine-tuned weights for inference:
python tools/train.py --config-file configs/ViTAEv2_S/rumsey/test.yaml --num-gpus 2 --eval-only
JSON results will be saved in output/vitaev2/test/rumsey_bs2_test_final/inference/text_results.json, and you can use tools/convert_to_original.py to convert the JSON file to submission results.
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.