Off-line Thai handwriting recognition in legal amount

Chatwiriya, Watchara; Klinkhachorn, P.

doi:10.33915/etd.1663

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…From the results in Tables II and III, it can be seen that MDF yielded better recognition rates when applied to ANNs, and GGF yielded better recognition rates when applied to the SVM. As mentioned earlier, most of the Thai language recognition techniques found in the literature were performed based on character recognition [17]. Feature extraction techniques such as the transition profile, Fourier descriptor, and edge direction, etc.…”

Section: Resultsmentioning

confidence: 99%

Off-line handwritten Thai name recognition for student identification in an automated assessment system

Suwanwiwat

Nguyen

Blumenstein

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier).

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Section: Resultsmentioning

confidence: 99%

Off-line handwritten Thai name recognition for student identification in an automated assessment system

Suwanwiwat

Nguyen

Blumenstein

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Off-Line Handwritten Bilingual Name Recognition for Student Identification in an Automated Assessment System

Suwanwiwat

Nguyen

Blumenstein

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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The Student name Identification System (SIS) proposed here was investigated for English and Thai languages combined. The proposed system recognises each name by using an approach for whole word recognition. In the proposed system, the Gaussian Grid Feature (GGF), and Modified Direction Feature (MDF), together with a proposed hybrid feature extraction technique called Water Reservoir, Loop and Gaussian Grid Feature (WRLGGF) were investigated on full word contour images of each name sample; Artificial neural networks and support vector machines were used as classifiers. An encouraging recognition accuracy of 99.25% was achieved employing the proposed technique compared to 98.59% for GGF, and 96.63% using MDF.

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Off-line Thai handwriting recognition in legal amount

Cited by 2 publications

References 24 publications

Off-line handwritten Thai name recognition for student identification in an automated assessment system

Off-line handwritten Thai name recognition for student identification in an automated assessment system

Off-Line Handwritten Bilingual Name Recognition for Student Identification in an Automated Assessment System

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