Image-to-Markup Generation with Coarse-to-Fine Attention
Abstract
We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism. Our method is evaluated in the context of image-to-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTC-based models, attention-based approaches can tackle this non-standard OCR task. Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data. To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention.
Cite
@article{arxiv.1609.04938,
title = {Image-to-Markup Generation with Coarse-to-Fine Attention},
author = {Yuntian Deng and Anssi Kanervisto and Jeffrey Ling and Alexander M. Rush},
journal= {arXiv preprint arXiv:1609.04938},
year = {2017}
}
Comments
Accepted by ICML 2017