This repository contains the source code for TAP introduced in the following papers:
- v1: A gru-based encoder-decoder approach with attention for online handwritten mathematical expression recognition
- v2: Track, attend and parse (TAP): An end-to-end framework for online handwritten mathematical expression recognition
Here, v1 employs the coverage based spatial attention model, v2 employs the guided hybrid attention model.
- Install cuda-8.0 cudnn-v7
- Install Theano.0.10.0 with libgpuarray
If you find TAP useful in your research, please consider citing:
@inproceedings{zhang2017icdar,
title={A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition},
author={Jianshu Zhang and Jun Du and Lirong Dai},
booktitle={International Conference on Document Analysis and Recognition},
volume={1},
pages={902--907},
year={2017}
}
@article{zhang2019track,
title={Track, Attend and Parse (TAP): An End-to-end Framework for Online Handwritten Mathematical Expression Recognition},
author={Zhang, Jianshu and Du, Jun and Dai, Lirong},
journal={IEEE Transactions on Multimedia},
volume={21},
number={1},
pages={221--233},
year={2019}
}
-
Train TAP without using weightnoise and save the best model in terms of WER
$ bash train.sh -
Anneal the best model by using weightnoise and save the new best model
$ bash train_weightnoise.sh -
Reload the new best model and generate the testing latex strings
$ bash test.sh
xysszjs at mail.ustc.edu.cn
West campus of University of Science and Technology of China
Any discussions, suggestions and questions are welcome!