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To reduce the classification errors of online handwritten Japanese character recognition, we propose a method for confusing characters discrimination with little additional costs. After building confusing sets by cross validation using a baseline quadratic classifier, a logistic regression (LR) classifier is trained to discriminate the characters in each set. The LR classifier uses subspace features...
This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple candidates of character patterns are generated by over-segmentation, and sequences of...
The HMM-based segmentation-free strategy for Chinese handwriting recognition has the advantage of training without annotation of character boundaries. However, the recognition performance has been limited by the small number of string samples. In this paper, we explore two techniques to improve the performance. First, Delta features are added to the static ones for alleviating the conditional independence...
The alignment of text line images with text transcript is a crucial step of handwritten document annotation. Handwritten text alignment is prone to errors due to the difficulty of character segmentation and the variability of character shape, size and position. In this paper, we propose to incorporate the geometric context of character strings to improve the alignment accuracy for offline handwritten...
Chinese handwriting recognition remains a challenge. Research works have reported very high accuracies on neatly handwritten characters yet the performance on unconstrained handwriting remains quite low. To promote the recognition technology, new databases of unconstrained handwriting have been constructed for academic research and benchmarking. This paper reports the contest results of online and...
This paper describes a method of online handwritten Japanese text recognition by improved path evaluation. Based on a theoretical ground, the method evaluates the likelihood of candidate segmentation paths by combining scores of character pattern size, inner gap, character recognition, single and pair character position, candidate segmentation point and linguistic context, with the weight parameters...
This paper describes a publicly available database, CASIA-OLHWDB1, for research on online handwritten Chinese character recognition. This database is the first of our series of online/offline handwritten characters and texts, collected using Anoto pen on paper. It contains unconstrained handwritten characters of 4,037 categories (3,866 Chinese characters and 171 symbols) produced by 420 persons, and...
This paper describes an online handwritten Japanese character string recognition system based on conditional random fields, which integrates the information of character recognition, linguistic context and geometric context in a principled framework, and can effectively overcome the variable length of candidate segmentation. For geometric context, we employ both unary and binary feature functions,...
Directional features are preferred in off-line Chinese character recognition due to their superior performance. This paper proposes an enhanced four plane feature (en-FPF) within a segmentation-free recognition framework. First, the directional planes are strengthened by replenishing salient pixels. Second, the method to count perpendicular strokes are renewed. In experiments of realistic Chinese...
This paper proposes a new radical-based approach for online handwritten chinese character recognition. The approach is novel in three respects: statistical classification of radicals, over-segmentation of characters into candidate radicals, and lexicon-driven recognition of characters. Currently, we have applied the approach to Chinese characters of left-right structure and are extending to other...
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