Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Brahmi Word Recognition by Supervised Techniques

Version 1 : Received: 4 June 2020 / Approved: 5 June 2020 / Online: 5 June 2020 (14:03:45 CEST)

How to cite: Gautam, N.; See Chai, S.; Afrin, S.; Jose, J. Brahmi Word Recognition by Supervised Techniques. Preprints 2020, 2020060048 (doi: 10.20944/preprints202006.0048.v1). Gautam, N.; See Chai, S.; Afrin, S.; Jose, J. Brahmi Word Recognition by Supervised Techniques. Preprints 2020, 2020060048 (doi: 10.20944/preprints202006.0048.v1).

Abstract

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

Subject Areas

Pattern Recognition; Feature extraction; SVM; HOG; Zonal density

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