DocumentCode :
3486317
Title :
Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition
Author :
Bluche, Theodore ; Ney, Hermann ; Kermorvant, Christopher
Author_Institution :
A2iA SA, Paris, France
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
285
Lastpage :
289
Abstract :
In this paper, we show that learning features with convolutional neural networks is better than using hand-crafted features for handwritten word recognition. We consider two kinds of systems: a grapheme based segmentation and a sliding window segmentation. In both cases, the combination of a convolutional neural network with a HMM outperform a state-of-the art HMM system based on explicit feature extraction. The experiments are conducted on the Rimes database. The systems obtained with the two kinds of segmentation are complementary: when they are combined, they outperform the systems in isolation. The system based on grapheme segmentation yields lower recognition rate but is very fast, which is suitable for specific applications such as document classification.
Keywords :
feature extraction; handwriting recognition; image segmentation; neural nets; visual databases; Rimes database; convolutional neural networks; document classification; grapheme based segmentation; handwritten word recognition; learning feature extraction; sliding window segmentation; Feature extraction; Handwriting recognition; Hidden Markov models; Image segmentation; Neural networks; Principal component analysis; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location :
Washington, DC
ISSN :
1520-5363
Type :
conf
DOI :
10.1109/ICDAR.2013.64
Filename :
6628629
Link To Document :
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