Baybayin Character Instance Detection

TL;DR

This paper introduces a novel end-to-end computer vision system for detecting and recognizing Baybayin characters in images, supporting the promotion of the script through improved recognition accuracy and handling of diacritics.

Contribution

We propose the first Baybayin character instance detection model using CNNs, surpassing existing methods in accuracy and robustness for real-world applications.

Findings

Achieved a mAP50 score of 93.30%

Attained an F1-Score of 84.84%

Demonstrated superior performance over existing methods

Abstract

The Philippine Government recently passed the "National Writing System Act," which promotes using Baybayin in Philippine texts. In support of this effort to promote the use of Baybayin, we present a computer vision system which can aid individuals who cannot easily read Baybayin script. In this paper, we survey the existing methods of identifying Baybayin scripts using computer vision and machine learning techniques and discuss their capabilities and limitations. Further, we propose a Baybayin Optical Character Instance Segmentation and Classification model using state-of-the-art Convolutional Neural Networks (CNNs) that detect Baybayin character instances in an image then outputs the Latin alphabet counterparts of each character instance in the image. Most existing systems are limited to character-level image classification and often misclassify or not natively support characters with diacritics. In addition, these existing models often have specific input requirements that limit it to classifying Baybayin text in a controlled setting, such as limitations in clarity and contrast, among others. To our knowledge, our proposed method is the first end-to-end character instance detection model for Baybayin, achieving a mAP50 score of 93.30%, mAP50-95 score of 80.50%, and F1-Score of 84.84%.