Online Handwritten Japanese Character String Recognition Using Conditional Random Fields

Zhou, Xiangdong; Liu, Cheng‐Lin; Nakagawa, Masaki

doi:10.1109/icdar.2009.95

Cited by 20 publications

(12 citation statements)

References 11 publications

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“…Similar to the integrated segmentation and recognition approach for character string recognition [14], before adjusting the character boundaries, the input string X is over-segmented into a sequence of components ( Figure. 1 (a)).…”

Section: Crfs For Transcript Mappingmentioning

confidence: 99%

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Transcript Mapping for Handwritten Text Lines Using Conditional Random Fields

Zhou

Yin

Wang

et al. 2011

2011 International Conference on Document Analysis and Recognition

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This paper presents a conditional random field (CRF) model for aligning online handwritten Chinese/Japanese text lines (character strings) with the corresponding transcripts. The CRF model is defined on a lattice which contains all possible segmentation hypotheses. The feature functions characterize the shape and context dependences of characters, including the scores of character recognition and the geometric compatibilities between characters. The combining parameters are optimized by energy minimization. Experimental results on two online databases: CASIA-OLHWDB and TUAT Kondate demonstrate the effectiveness of the proposed method.

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Section: Crfs For Transcript Mappingmentioning

confidence: 99%

“…Existing Chinese/Japanese string recognition algorithms usually adopt segmented text lines for parameter learning [14], [15]. Transcript mapping enables training with un-segmented text line data and alleviates the pains taken in human segmentation.…”

Section: Introductionmentioning

confidence: 99%

Transcript Mapping for Handwritten Text Lines Using Conditional Random Fields

Zhou

Yin

Wang

et al. 2011

2011 International Conference on Document Analysis and Recognition

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“…Introducing weighting parameters to MRFs and optimizing them based on CRFs [7] or MCE [8] may bring even higher recognition accuracy; CRF has been successfully applied to on-line string and off-line word recognition [9,10].…”

Section: Introductionmentioning

confidence: 99%

On-line Handwritten Japanese Characters Recognition Using a MRF Model with Parameter Optimization by CRF

Zhu

Nakagawa

2011

2011 International Conference on Document Analysis and Recognition

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This paper describes a Markov random field (MRF) model with weighting parameters optimized by conditional random field (CRF) for on-line recognition of handwritten Japanese characters. It also presents updated evaluation using a large testing set. The model extracts feature points along the pen-tip trace from pen-down to pen-up and sets each feature point from an input pattern as a site and each state from a character class as a label. It employs the coordinates of feature points as unary features and the differences in coordinates between the neighboring feature points as binary features. The weighting parameters are estimated by CRF or the minimum classification error (MCE) method. In experiments using the TUAT Kuchibue database, the method achieved a character recognition rate of 92.77%, which is higher than the previous model's rate, and the method of estimating the weighting parameters using CRF was more accurate than using MCE.

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“…Handwritten sentence (character string) recognition is a difficult contextual classification problem involving character segmentation and recognition, and has been attacked by many researchers [1][2][3][4][5][6]. A feasible approach is the oversegmentation-based recognition fusing character recognition scores, linguistic context and geometric context [5,6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Text Line Segmentation for Real-Time Recognition of Chinese Handwritten Sentences

Wang

Liu

2011

2011 International Conference on Document Analysis and Recognition

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Real-time recognition of handwritten sentences enables fast text input but the dynamic nature of writing makes reliable text line segmentation difficult. This paper proposes a method for real-time dynamic text line segmentation of online Chinese handwriting. The core of the method is a statistical classifier for modeling the geometric relationship between an ongoing stroke and the previous text lines, to assign the stroke into a previous line or form a new line. The method can deal with delayed strokes and therefore enables robust real-time recognition. We evaluated the segmentation performance on a dataset of online Chinese handwriting by simulating the real-time writing and recognition process. The experimental results demonstrate the effectiveness and robustness of the proposed method.

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Online Handwritten Japanese Character String Recognition Using Conditional Random Fields

Cited by 20 publications

References 11 publications

Transcript Mapping for Handwritten Text Lines Using Conditional Random Fields

Transcript Mapping for Handwritten Text Lines Using Conditional Random Fields

On-line Handwritten Japanese Characters Recognition Using a MRF Model with Parameter Optimization by CRF

Dynamic Text Line Segmentation for Real-Time Recognition of Chinese Handwritten Sentences

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