  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%.
