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A Baybayin word recognition system.

Rodney Pino1, Renier Mendoza1, Rachelle Sambayan1

  • 1Institute of Mathematics, University of the Philippines Diliman, Quezon City, Metro Manila, Philippines.

Peerj. Computer Science
|July 9, 2021
PubMed
Summary

This study introduces a new algorithm for Baybayin Optical Character Recognition (OCR), achieving 97.9% accuracy in transliterating Baybayin words into Latin script. It

Keywords:
BaybayinBaybayin word recognitionOptical character recognitionSupport vector machine

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Philippine Linguistics

Background:

  • Baybayin is a pre-Hispanic Philippine script gaining renewed interest.
  • Existing Baybayin Optical Character Recognition (OCR) research focuses on character-level recognition.
  • Recent legislation promotes Baybayin as the national writing system, increasing the need for advanced recognition technologies.

Purpose of the Study:

  • To develop and evaluate an algorithm for word-level Baybayin to Latin script transliteration from images.
  • To address the limitations of current character-level Baybayin OCR systems.
  • To create a system capable of automated transliteration for various applications.

Main Methods:

  • Utilized a Support Vector Machine (SVM) for Baybayin character classification.
  • Developed a process involving isolation, classification, and concatenation of individual Baybayin characters.
  • Tested the algorithm on a novel dataset of Baybayin word images.

Main Results:

  • Achieved a high recognition accuracy of 97.9% for Baybayin word transliteration.
  • Demonstrated the first successful word-level recognition of Baybayin scripts.
  • Validated the effectiveness of the SVM-based classification approach.

Conclusions:

  • The proposed system offers a significant advancement in Baybayin OCR by enabling word-level transliteration.
  • This technology has practical applications in digitizing historical texts, signage, and graphic designs.
  • The high accuracy suggests the potential for widespread adoption in automated transliteration tasks.