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21 June 2013 New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks
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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.
© 2013 SPIE and IS&T 0091-3286/2013/$25.00 © 2013 SPIE and IS&T
Olivier Morillot, Laurence Likforman-Sulem, and Emmanuèle Grosicki "New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks," Journal of Electronic Imaging 22(2), 023028 (21 June 2013). https://doi.org/10.1117/1.JEI.22.2.023028
Published: 21 June 2013
JOURNAL ARTICLE
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