A robust model for on-line handwritten japanese text recognition

This paper describes a robust model for on-line handwritten Japanese text recognition. The method evaluates the likelihood of candidate segmentation paths by combining scores of character pattern size, inner gap, character recognition, single-character position, pair-character position, likelihood of candidate segmentation point and linguistic context. The path score is insensitive to the number of candidate patterns and the optimal path can be found by the Viterbi search. In experiments of handwritten Japanese sentence recognition, the proposed method yielded superior performance.

• Type: Journal
• Year: 2010
• Where: IJDAR
• Authors: Bilan Zhu, Xiang-Dong Zhou, Cheng-Lin Liu, Masaki Nakagawa
• Keywords: Candidate Segmentation, Candidate Segmentation Paths, Candidate Segmentation Point, IJDAR 2010