A Stroke Shape and Structure Based Approach for Off-line Chinese Handwriting Identification

Tan, Jun; Lai, Jianhuang; Wang, Chang-dong; Feng, Ming-Shuai

doi:10.5815/ijisa.2011.02.01

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Some feature types include direction, curvature, chain code, gradient direction, textural and allographic features (Bulacu and Schomaker, 2007), and wavelet domain local binary patterns (WD-LBP) (Du et al, 2010). For Chinese handwriting, features such as Fourier transform, shape and structure (Tan et al, 2011), Gabor filter (Tan, 1992), normalization-cooperated gradient (Liu, 2007), fragmented edge structure (Wen et al, 2012), and weighted grid micro-structure (Xu et al, 2011) have been explored.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

“…We list the various feature types in Table 2. Feature Type Name #of Features Transformed or Geometrical Text-Independent Data Set TortuosityHist (Abbasi et al, 2013) 10 Geometrical Yes ICDAR 2013 Direction (Blumenstein et al, 2007) 40 Geometrical Yes ICDAR 2013 Curvature (Siddiqi and Vincent, 2010) 900 Geometrical Yes ICDAR 2013 Chain Code (Siddiqi and Vincent, 2009) 5020 Geometrical Yes ICDAR 2013 Gradient Direction (Liu, 2007) 1096 Transformed Yes ICDAR 2013 Fourier (Chen et al, 2008) 1600 Transformed Yes RDF Gabor (Tan, 1992) 36 Transformed Yes RDF GMSF (Li and Ding, 2009) 6561 Geometrical Yes RDF Allographic (Bulacu and Schomaker, 2007) 1296 Geometrical Yes RDF Shape+Structure (Tan et al, 2011) 24 Geometrical No RDF…”

Section: Data Setsmentioning

confidence: 99%

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A multi-feature selection approach for gender identification of handwriting based on kernel mutual information

Suen

Nobile

et al. 2019

Pattern Recognition Letters

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This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features like slant, curvature, line separation, chain code, character shapes, and more, can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes an approach, named Kernel Mutual Information (KMI), that focuses on feature selection. The KMI approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. To ensure that KMI can apply the various features, this paper describes the handwriting segmentation and handwritten text recognition technology used. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, which provides the samples in both Arabic and English. The other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting.

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Section: A C C E P T E D Mmentioning

confidence: 99%

Section: Data Setsmentioning

confidence: 99%

A multi-feature selection approach for gender identification of handwriting based on kernel mutual information

Suen

Nobile

et al. 2019

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several studies have focused on the design of a pen or device that can identify characters input via a digital interface. Although these designs are not directly related to solving conventional handwriting issues, their concepts and technologies, including algorithms, data acquisition schemes, and force sensors, can be applied to the investigation of handwriting based on biomechanical perspectives [16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%