Browsing by Author "Marukatat, Sanparith"

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Recognition of handwritten Lanna Dhamma characters using a set of optimally designed moment features
(2017-12-01)
Inkeaw, Papangkorn
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Charoenkwan, Phasit
;
Huang, Hui-Ling
;
Marukatat, Sanparith
;
Ho, Shinn-Ying
Lanna Dhamma alphabet was used mainly for religious communication in the ancient Lanna Kingdom of Thailand. The old manuscripts using this alphabet are gradually decayed. It is desirable to preserve these valuable manuscripts in machine-encoded text files. Existing works used optical character recognition (OCR) methods based on wavelet transform for recognition of handwritten Lanna Dhamma characters. However, the test accuracy of writer-independent recognition is not satisfactory. This work proposes an OCR method, called LDIMS, for recognition of handwritten Lanna Dhamma characters using a set of optimally designed moment features. The LDIMS using an optimization approach to feature selection consists of three main phases: (1) determination of moment orders for each of eight effective moment descriptors, (2) the best combination of selected moment descriptors and (3) the optimized selection of moment features using an inheritable bi-objective genetic algorithm. The LDIMS has three individual feature sets for the recognition of handwritten Lanna Dhamma characters in upper, middle and lower levels. The character images gleaned from previous work were used as a training dataset. A new character image dataset from different writers was established for evaluating ability of writer-independent recognition. The experimental results show that the LDIMS using four moment descriptors, Meixner, Charlier, Tchebichef and Hahn, has test accuracies of 86.60, 74.38 and 85.82% for the characters in upper, middle and lower levels, respectively. The LDIMS with a mean accuracy of 82.27% performed well in recognizing the handwritten Lanna Dhamma characters from new writers, compared to existing methods using generic descriptors in terms of both accuracy and feature number used. Experimental results show that the generalized OCR method, LDIMS, is also effective for character recognition of digit and English alphabets, compared to existing methods.
Recognition-based character segmentation for multi-level writing style
(2018-06-01)
Inkeaw, Papangkorn
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Bootkrajang, Jakramate
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Charoenkwan, Phasit
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Marukatat, Sanparith
;
Ho, Shinn-Ying
Character segmentation is an important task in optical character recognition (OCR). The quality of any OCR system is highly dependent on character segmentation algorithm. Despite the availability of various character segmentation methods proposed to date, existing methods cannot satisfyingly segment characters belonging to some complex writing styles such as the Lanna Dhamma characters. In this paper, a new character segmentation method named graph partitioning-based character segmentation is proposed to address the problem. The proposed method can deal with multi-level writing style as well as touching and broken characters. It is considered as a generalization of existing approaches to multi-level writing style. The proposed method consists of three phases. In the first phase, a newly devised over-segmentation technique based on morphological skeleton is used to obtain redundant fragments of a word image. The fragments are then used to form a segmentation hypotheses graph. In the last phase, the hypotheses graph is partitioned into subgraphs each corresponding to a segmented character using the partitioning algorithm developed specifically for character segmentation purpose. Experimental results based on handwritten Lanna Dhamma characters datasets showed that the proposed method achieved high correct segmentation rate and outperformed existing methods for the Lanna Dhamma alphabet.
Rule-Based Page Segmentation for Palm Leaf Manuscript on Color Image
(2016)
Inkeaw, Papangkorn
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Bootkrajang, Jakramate
;
Charoenkwan, Phasit
;
Marukatat, Sanparith
;
Ho, Shinn-Ying
Palm leaf manuscripts are important source of history and ancient wisdom. Large number of manuscripts have been already digitized in the form of folio images. To extract useful information, an optical character recognition (OCR) is often considered to be the first step towards text mining. Unfortunately, folio images contain multiple unsegmented palm leaf images, making it difficult to manage in OCR process. This motivates us to propose a new page segmentation method for palm leaf manuscripts. This method consists of two main steps, first of which is the detection of objects in folio images using Connected Component Labeling method in a transformed L*a*b* color space. The second step is rule- based selection of objects as either palm leaf or not palm leaf. The experiments performed on 20 publicly available palm leaf manuscripts composed of 384 folio images demonstrated that the proposed method effectively segmented folio images into separate palm leaf images, with 99.86% precision and 96.67% recall scores.