Skip to Main content Skip to Navigation
Conference papers

Applying End-to-end Trainable Approach on Stroke Extraction in Handwritten Math Expressions Images

Abstract : In this paper, we propose a novel end-to-end system to extract strokes from offline math expressions. Using a multi-task neural network we simultaneously predict the location of the pen and the pen state. Our approach is based on a recent state-of-the-art image-to-sequence method limited to small fixed-sizes images. We generalize it to large and multi-symbol images without preprocessing steps such as skeletonization or binarization. This architecture allows an end-to-end training. A curriculum learning strategy have been used to address the complexity of the images. We achieve comparable results to the state of the art on the UNIPEN English character dataset considering the next point prediction. We propose a stroke level metrics that allows us to measure the stroke reconstruction. Experiments show the advantages and limitations of the adopted Image-to-Sequence method when scaling up to large and complex images such as math equations.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03236506
Contributor : Harold Mouchère <>
Submitted on : Wednesday, May 26, 2021 - 11:50:35 AM
Last modification on : Friday, May 28, 2021 - 3:08:47 AM

Identifiers

  • HAL Id : hal-03236506, version 1

Citation

Elmokhtar Moussa, Thibault Lelore, Harold Mouchère. Applying End-to-end Trainable Approach on Stroke Extraction in Handwritten Math Expressions Images. ICDAR 2021 : 16th International Conference on Document Analysis and Recognition, Sep 2021, Lausanne, Switzerland. ⟨hal-03236506⟩

Share

Metrics

Record views

27