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New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

[+] Author Affiliations
Olivier Morillot

Institut Mines-Télécom/Télécom ParisTech, 46 rue Barrault, 75013 Paris, France

Laurence Likforman-Sulem

Institut Mines-Télécom/Télécom ParisTech, 46 rue Barrault, 75013 Paris, France

Emmanuèle Grosicki

DGA Ingénierie des Projets, 7-9 rue des Mathurins, 92220 Bagneux, France

J. Electron. Imaging. 22(2), 023028 (Jun 21, 2013). doi:10.1117/1.JEI.22.2.023028
History: Received December 21, 2012; Revised March 30, 2013; Accepted May 7, 2013
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Abstract.  Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

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Citation

Olivier Morillot ; Laurence Likforman-Sulem and Emmanuèle Grosicki
"New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks", J. Electron. Imaging. 22(2), 023028 (Jun 21, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.2.023028


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