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

Olivier Morillot; Laurence Likforman-Sulem; Emmanuèle Grosicki

doi:10.1117/1.JEI.22.2.023028

21 June 2013 New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

Olivier Morillot, Laurence Likforman-Sulem, Emmanuèle Grosicki

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 2, 023028 (June 2013). https://doi.org/10.1117/1.JEI.22.2.023028

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available