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Proceedings Paper

A comparison of 1D and 2D LSTM architectures for the recognition of handwritten Arabic
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Paper Abstract

In this paper, we present an Arabic handwriting recognition method based on recurrent neural network. We use the Long Short Term Memory (LSTM) architecture, that have proven successful in different printed and handwritten OCR tasks. Applications of LSTM for handwriting recognition employ the two-dimensional architecture to deal with the variations in both vertical and horizontal axis. However, we show that using a simple pre-processing step that normalizes the position and baseline of letters, we can make use of 1D LSTM, which is faster in learning and convergence, and yet achieve superior performance. In a series of experiments on IFN/ENIT database for Arabic handwriting recognition, we demonstrate that our proposed pipeline can outperform 2D LSTM networks. Furthermore, we provide comparisons with 1D LSTM networks trained with manually crafted features to show that the automatically learned features in a globally trained 1D LSTM network with our normalization step can even outperform such systems.

Paper Details

Date Published: 8 February 2015
PDF: 10 pages
Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020H (8 February 2015); doi: 10.1117/12.2075930
Show Author Affiliations
Mohammad Reza Yousefi, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (Germany)
Mohammad Reza Soheili, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (Germany)
Thomas M. Breuel, Technische Univ. Kaiserslautern (Germany)
Didier Stricker, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (Germany)

Published in SPIE Proceedings Vol. 9402:
Document Recognition and Retrieval XXII
Eric K. Ringger; Bart Lamiroy, Editor(s)

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