An Investigation of Imaginary Stroke Techinique for Cursive Online Handwriting Chinese Character Recognition

Ding, Kai; Deng, Guoqiang; Jin, Lianwen

doi:10.1109/icdar.2009.29

Cited by 18 publications

(13 citation statements)

References 8 publications

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“…Since the LDA, also known as Fisher Discriminative Analysis (FDA), is widely used approach in the character recognition applications [1,3,4,11,12,14], in this section, we focus on how to incremental learning MQDF classifier in the LDA feature space.…”

Section: Discriminative Imqdf (Dimqdf)mentioning

confidence: 99%

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Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

Ding

Jin

2010

2010 12th International Conference on Frontiers in Handwriting Recognition

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Writer adaptation has been proved to be an effective approach to improve the recognition performance of the writer-independent recognizer for a particular writer. In this paper, we propose a writer adaptive handwriting recognition approach by incremental learning the Modified Quadratic Discriminant Function (MQDF) classifier. We derived the solution of Incremental MQDF (IMQDF) and then present a Discriminative IMQDF (DIMQDF) by deriving the solution of IMQDF in the updated discriminative feature space. Based on IMQDF or DIMQDF, the writer adaptation is finally performed by updating the MQDF recognizer adaptively. The experimental results for recognizing handwriting Chinese characters indicate that the proposed IMQDF and DIQMDF approaches can reduce as much as 52.71% and 45.38% error rate respectively on the writer-dependent dataset while only have less than 0.18% accuracy loss on the writer-independent dataset. In other words, the proposed IMQDF and DIMQDF based writer adaptation approaches can significantly increase the recognition accuracy on writer-dependent dataset while only have limited negative influence for general writer.

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Section: Discriminative Imqdf (Dimqdf)mentioning

confidence: 99%

“…Motivated by these, many researchers devoted themselves to the field of handwriting character recognition and achieved great progress during the past 40 years [1][2][3]. However, recent researches [4][5][6] have shown that the recognition accuracy using state-of-the-art techniques cannot satisfy user's expectations [7].…”

Section: Introductionmentioning

confidence: 99%

Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

Ding

Jin

2010

2010 12th International Conference on Frontiers in Handwriting Recognition

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“…two sets of sequences). A traditional method is to concatenate the strokes using the handwritten order [14]. Therefore, the distance between two multi-stroke symbols can be computed using DTW [2].…”

Section: Dtw Between Two Sets Of Sequencesmentioning

confidence: 99%

A Multi-stroke Dynamic Time Warping Distance Based on A* Optimization

Mouchère

Viard-Gaudin

et al. 2013

2013 12th International Conference on Document Analysis and Recognition

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Dynamic Time Warping (DTW) is a famous distance to compare two mono-stroke symbols. It obeys the boundary and continuity constraints. The extension to multi-stroke symbols raises specific problems. A naïve solution is to convert the multi-stroke symbol into a single one by a direct concatenation respecting the handwriting order. However, people may write a symbol with different stroke orders and different stroke directions. Applying a brute force method by searching all the possible directions and orders leads to prohibitive calculation times. To reduce the searching complexity, we propose the DTW-A* algorithm that keeps the continuity constraint during each partial matching. This DTW-A* distance achieves the best recognition rate and the best stability in cross-validation when comparing three distances (DTW-A*, DTW, Modified Hausdorff Distance) on a flowchart dataset which mainly contains multistroke symbols.

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“…For representing a character sample, we used a benchmark feature extraction method [57]: eightdirection histogram feature extraction combined with pseudo-2D bimoment normalization (P2DBMN). We also added the direction values of off-strokes (pen lifts) to real strokes with a weight of 0.5 [58]. The feature dimensionality is 512, which is reduced to 160 by Fisher linear discriminant analysis (FLDA).…”

Section: Databasementioning

confidence: 99%

Writer Adaptation with Style Transfer Mapping

Zhang

Liu

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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Adapting a writer-independent classifier toward the unique handwriting style of a particular writer has the potential to significantly increase accuracy for personalized handwriting recognition. This paper proposes a novel framework of style transfer mapping (STM) for writer adaptation. The STM is a writer-specific class-independent feature transformation which has a closed-form solution. After style transfer mapping, the data of different writers are projected onto a style-free space, where the writer-independent classifier needs no change to classify the transformed data and can achieve significantly higher accuracy. The framework of STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation, where writer-specific data can be either labeled or unlabeled and need not cover all classes. In this paper, we combine STM with the state-of-the-art classifiers for large-category Chinese handwriting recognition: learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF). Experiments on the online Chinese handwriting database CASIA-OLHWDB demonstrate that STM-based adaptation is very efficient and effective in improving classification accuracy. Semi-supervised adaptation achieves the best performance, while unsupervised adaptation is even better than supervised adaptation. On handwritten text data, semi-supervised adaptation achieves error reduction rates 31.95 and 25.00 percent by LVQ and MQDF, respectively.

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An Investigation of Imaginary Stroke Techinique for Cursive Online Handwriting Chinese Character Recognition

Cited by 18 publications

References 8 publications

Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

A Multi-stroke Dynamic Time Warping Distance Based on A* Optimization

Writer Adaptation with Style Transfer Mapping

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