A Candidate Lattice Refinement Method for Online Handwritten Japanese Text Recognition

This paper presents a candidate lattice refinement method for online handwritten Japanese text recognition. In the integrated segmentation-recognition framework, we first over-segment a character string pattern into primitive segments at least at their true boundaries so that each primitive segment may compose a single character or a part of a character. Then a candidate lattice is constructed based on the primitive segments. We search within the candidate lattice to obtain the optimal path as recognition result. In striving for high recognition accuracy, however, the approach must generate many candidate lattice nodes, which ultimately increase the recognition time. To solve this problem, we refine the candidate lattice to eliminate unnecessary nodes before path search and text recognition. For the refinement, we evaluate all segmentation hypotheses by combining the probability of a character verifier using noncharacter samples, the class-independent unary and binary geometric context, as well as character segmentation. We retain N-best paths by beam search to reduce the complexity of the candidate lattice. Experiments on horizontal text lines extracted from the Kondate database show that the proposed method keeps recognition accuracy while reducing recognition time to half.

[1]  Lian-Wen Jin,et al.  A Bayesian-based method of unconstrained handwritten offline Chinese text line recognition , 2011, International Journal on Document Analysis and Recognition (IJDAR).

[2]  Bilan Zhu,et al.  Character-Position-Free On-Line Handwritten Japanese Text Recognition by Two Segmentation Methods , 2016, IEICE Trans. Inf. Syst..

[3]  中川 正樹,et al.  A Database of On-line Handwritten Mixed Objects named "Kondate" (パターン認識・メディア理解) , 2014 .

[4]  Fei Yin,et al.  Transcript mapping for handwritten Chinese documents by integrating character recognition model and geometric context , 2013, Pattern Recognit..

[5]  Online Handwritten Japanese Character String Recognition Using Conditional Random Fields , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[6]  Masaki Nakagawa,et al.  Online Handwritten Chinese/Japanese Character Recognition , 2012 .

[7]  Masaki Nakagawa,et al.  A robust model for on-line handwritten japanese text recognition , 2010, Electronic Imaging.

[8]  Cheng-Lin Liu,et al.  An approach for real-time recognition of online Chinese handwritten sentences , 2012, Pattern Recognit..

[9]  Hiroshi Sako,et al.  Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Yi-Chao Wu,et al.  Evaluation of Geometric Context Models for Handwritten Numeral String Recognition , 2014, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[11]  Cheng-Lin Liu,et al.  Online Japanese Character Recognition Using Trajectory-Based Normalization and Direction Feature Extraction , 2006 .

[12]  Masaki Nakagawa,et al.  Collection of on-line handwritten Japanese character pattern databases and their analyses , 2004, Document Analysis and Recognition.

[13]  Masaki Nakagawa,et al.  Objective Function Design for MCE-Based Combination of On-line and Off-line Character Recognizers for On-line Handwritten Japanese Text Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[14]  Masaki Nakagawa,et al.  A robust method for coarse classifier construction from a large number of basic recognizers for on-line handwritten Chinese/Japanese character recognition , 2014, Pattern Recognit..