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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.
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Contributor : Harold Mouchère Connect in order to contact the contributor
Submitted on : Wednesday, September 29, 2021 - 1:49:38 PM
Last modification on : Wednesday, April 27, 2022 - 3:42:54 AM
Long-term archiving on: : Thursday, December 30, 2021 - 7:12:59 PM


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Elmokhtar Mohamed 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. ⟨10.1007/978-3-030-86334-0_29⟩. ⟨hal-03236506⟩



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