Segmentation of On-Line Freely Written Japanese Text Using SVM for Improving Text Recognition

Authors:
Bilan Zhu;Masaki Nakagawa
Affiliations:
-;-
Venue:
IEICE - Transactions on Information and Systems
Year:
2008

Citing 6
Cited 2

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An introduction to support Vector Machines: and other kernel-based learning methods
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ACM Transactions on Asian Language Information Processing (TALIP)
Building compact recognizer with recognition rate maintained for on-line handwritten Japanese text recognition

Pattern Recognition Letters

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Abstract

This paper describes a method of producing segmentation point candidates for on-line handwritten Japanese text by a support vector machine (SVM) to improve text recognition. This method extracts multi-dimensional features from on-line strokes of handwritten text and applies the SVM to the extracted features to produces segmentation point candidates. We incorporate the method into the segmentation by recognition scheme based on a stochastic model which evaluates the likelihood composed of character pattern structure, character segmentation, character recognition and context to finally determine segmentation points and recognize handwritten Japanese text. This paper also shows the details of generating segmentation point candidates in order to achieve high discrimination rate by finding the optimal combination of the segmentation threshold and the concatenation threshold. We compare the method for segmentation by the SVM with that by a neural network (NN) using the database HANDS-Kondate_t_bf-2001–11 and show the result that the method by the SVM bring about a better segmentation rate and character recognition rate.