Optical character recognition system for Baybayin scripts using support vector machine

In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines' national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates th...

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Bibliographic Details
Published in:PeerJ. Computer science 2021-02, Vol.7, p.e360-e360, Article e360
Main Authors: Pino, Rodney, Mendoza, Renier, Sambayan, Rachelle
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Baybayin
Baybayin script identification
Bills, Legislative
Classification
Computer Vision
Identification
Latin script identification
Natural Language and Speech
Neural networks
Optical character recognition
Optimization Theory and Computation
Recall
Scientific Computing and Simulation
Scripts
Support vector machine
Support vector machines
Wavelet transforms
Writing
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Summary:In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines' national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.360