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

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 combination of the segmentation threshold and the concatenation threshold. We compare the method for segmentation by the SVM with that by a neural network using the database HANDSKondate_t_b...

• Type: Conference
• Year: 2006
• Where: DAS
• Authors: Bilan Zhu, Junko Tokuno, Masaki Nakagawa
• Keywords: DAS 2006, Document Analysis, Handwritten Japanese Text, Segmentation Point, Segmentation Point Candidates