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2009
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Error-Correcting Output Coding for the Convolutional Neural Network for Optical Character Recognition

9 years 5 months ago
Error-Correcting Output Coding for the Convolutional Neural Network for Optical Character Recognition
It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N; 2) the minimum Hamming distance of the code words is designed to be as large as possible given N. ECOC provides the CNN with the ability to reject or correct output errors to reduce character insertions and substitutions in the recognized text. Also, using code words instead of letter images as the CNN target outputs makes it possible to construct an OCR for a new language without designing the letter images as the target outputs. Experiments on the recognition of English letters, 10 digits, and some special characters show the effectiveness of ECOC in reducing insertions and substitutions.
Huiqun Deng, George Stathopoulos, Ching Y. Suen
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICDAR
Authors Huiqun Deng, George Stathopoulos, Ching Y. Suen
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