Recognition of Handwritten Arabic Words with Dropout Applied in MDLSTM

Maalej, Rania; Kherallah, Monji

doi:10.1007/978-3-319-41501-7_83

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…It is named Multidimensional LSTM (MDLSTM) dedicated to the graph structure of an n-dimensional grid such as images [42]. MDLSTM model exhibits great performances on offline handwriting recognition tasks where the input is an image [47][48][49][50][51].…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

A tree-BLSTM-based recognition system for online handwritten mathematical expressions

Zhang

Mouchère

Viard-Gaudin

2018

Neural Comput & Applic

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Long short-term memory networks (LSTM) achieves great success in temporal dependency modelling for chain-structured data, such as texts and speeches. An extension towards more complex data structures as encountered in 2D graphic languages is proposed in this work. Specifically, we address the problem of handwritten mathematical expression recognition, using a Treebased BLSTM architecture allowing the direct labelling of nodes (symbol) and edges (relationship) from a graph modelling the input strokes. One major difference with the traditional approaches is that there is no explicit segmentation, recognition and layout extraction steps but a unique trainable system that produces directly a Stroke Label Graph describing a mathematical expression. The proposed system, considering no grammar, achieves competitive results in online math expression recognition domain.

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