Hybrid Feature Extraction and Multiclass SVM Based Ancient Character Recognition
1P.Balasubrmanie, Professor, Department of Computer Science & Engineering Kongu Engineering College, Perundurai – 638 052, Erode, Tamil Nadu, India.
2E.K.Vellingiriraj, Former Assistant Professor Department of Computer Science & Engineering Kongu Engineering College, Perundurai – 638 052, Erode, Tamil Nadu, India.
Manuscript received on 07 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 8407-8412 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6520098319/2019©BEIESP | DOI: 10.35940/ijrte.C6520.098319
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Abstract: Ancient character recognition is the most difficult task due to their different formats and less frequent knowledge about the ancient characters. This is performed in the previous research work namely Shape and Size aware Character Recognition and Conversion System (SSCR-CS). This research work proved better character recognition outcome. However this research work might be reduced in performance with lesser detailed information in the images. This is resolved in the proposed research work by introducing the most recent techniques for the character recognition outcome. This is attained by introducing the method namely Hybrid Feature Extraction and Multiclass SVM based recognition method (HFE-MCSVM). In this research work, initially image pre-processing is performed by using Gabor filter. After pre-processing segmentation of characters is performed by using overlapped character segmentation method. After segmentation character recognition is done by introducing the method namely hybrid feature extraction with Multiclass SVM classification approach. The overall assessment of the research work is done in the matlab simulation environment and then it is proved the proposed HFE-MCSVM shows better performance.
Keywords: Ancient Character Recognition, Feature Extraction, Segmentation of Characters, Hybrid Multiclass SVM
Scope of the Article: Pattern Recognition and Analysis