Regularized margin-based conditional log-likelihood loss for prototype learning

Jin, Xiao-Bo; Liu, Cheng‐Lin; Hou, Xinwen

doi:10.1016/j.patcog.2010.01.013

Cited by 62 publications

(25 citation statements)

References 25 publications

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“…In paper [34], some state-of-the-art methods are evaluated on the DB1.1. The implemented methods include advanced character normalization methods [6,[8][9][10], feature extraction methods [3,17] and classification methods [1,40,41]. In this table, CASIA-HWDB1.0 is another database released at the same time with DB1.1.…”

Section: Resultsmentioning

confidence: 99%

A character image restoration method for unconstrained handwritten Chinese character recognition

Shao

Wang

Xiao

2014

IJDAR

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Despite the success of methods on constrained handwriting databases, recognition of unconstrained handwritten Chinese characters remains a big challenge. One difficulty for recognizing unconstrained handwritting is that some connected strokes are involved or some strokes are omitted. In this paper, a character image restoration method is proposed for unconstrained handwritten Chinese character recognition. In this method, the observed character image is modeled as the combination of the ideal character image with two types of noise images: the omitted stroke noise image and the added stroke noise image. To preserve the original gradient features, restoration is done on the gradient features. The estimated features are then used to discriminate similar characters. To show the effectiveness of the proposed method, we extend some state-of-the-art classifiers based on the estimated features. Experimental results show that the extended classifiers outperform the original state-of-the-art classifiers. This demonstrates that the estimated features are useful for further improving the recognition rate.

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Section: Resultsmentioning

confidence: 99%

A character image restoration method for unconstrained handwritten Chinese character recognition

Shao

Wang

Xiao

2014

IJDAR

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“…The loss function used for DFE þLVQ learning can be MCE [30] criterion, while conditional log-likelihood loss (log-loss) [31] and other similar criteria can also be chosen. Here we use the log-loss.…”

Section: Related Workmentioning

confidence: 99%

Discriminative quadratic feature learning for handwritten Chinese character recognition

Zhou

Zhang

Yin

et al. 2016

Pattern Recognition

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“…(1) to increase the scores of correct retrievals results and decrease those incorrect retrievals. The conditional log likelihood (CLL) is widely used for estimating parameters in pattern classification [35].…”

Section: Estimation Of Parametersmentioning

confidence: 99%

Digital Ink Search Based on Character-Recognition Candidates Compared with Feature-Matching-Based Approach

Cheng

Zhu

Nakagawa

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents an approach based on character recognition to searching for keywords in on-line handwritten Japanese text. It employs an on-line character classifier and an off-line classifier or a combined classifier, which produce recognition candidates, and it searches for keywords in the lattice of candidates. It integrates scores to individually recognize characters and their geometric context. We use quadratic discriminant function(QDF) or support vector machines(SVM) models to evaluate the geometric features of individual characters and the relationships between characters. This paper also presents an approach based on feature matching that employs on-line or off-line features. We evaluate three recognition-based methods, two feature-matching-based methods, as well as ideal cases of the latter and concluded that the approach based on character recognition outperformed that based on feature matching.

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Regularized margin-based conditional log-likelihood loss for prototype learning

Cited by 62 publications

References 25 publications

A character image restoration method for unconstrained handwritten Chinese character recognition

A character image restoration method for unconstrained handwritten Chinese character recognition

Discriminative quadratic feature learning for handwritten Chinese character recognition

Digital Ink Search Based on Character-Recognition Candidates Compared with Feature-Matching-Based Approach

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