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Optical character recognition (OCR) is a state-of-the-art technology that allows automated detection and recognition of text from scanned documents and other images. While OCR has highly developed in popular writing systems like Roman, Brahmic, and Han scripts, there is presently a lack of technological integration for Baybayin scripts − a precolonial Filipino writing system. This gap in recent advancements may be due from the script’s antiquity. However, ongoing efforts led by Philippine government and cultural institutions, are actively working towards promoting Baybayin as a means of cultural revival and heritage preservation. In this work, a Matlab-based Baybayin OCR (MaBaybay-OCR) package is introduced to automate the process of transliterating the Baybayin to its modern Filipino Latin form. MaBaybay-OCR takes raw Baybayin text image as an input and performs image analytic strategies to generate correct transliteration results. The software system implements essential pre-processing techniques such as binarization, segmentation, and computation of the character’s features of interest to precisely isolate each Baybayin character. With the use of support vector machine (SVM) classifiers, the platform can accurately identify every Baybayin character’s corresponding Latin equivalent and can realize the Baybayin word transliteration by concatenating each character recognition result. The discrimination of Baybayin from Latin or Roman texts is a distinctive feature of this recognition package. All Matlab source codes are available in a public repository for reproducibility, and a user-friendly graphical user interface (GUI) for convenient usage is provided. To the best of our knowledge, this is the first software program that offers direct transliteration of Baybayin texts up to block-level. It is expected that this work will promote the Baybayin script and contribute towards its positive cultural exposure in the Philippines.

In response to the coronavirus disease 2019 (COVID-19) pandemic, the allocation of vaccines has become a crucial undertaking, particularly in the Philippines. The efficient distribution of vaccines across different regions remains vital. In this study, we present an approach to determine the optimal allocation of COVID-19 vaccines in the Philippines, leveraging actual data on the supply of vaccines per region for different brands. A notable advantage of our work, compared to existing models, is the comprehensive consideration of crucial factors, including vaccine effectiveness, waning immunity, various age groups, population data, and weekly vaccination rates. We formulate a linear programming model with the objective of minimizing both the primary and breakthrough infections. Several constraints are incorporated into the model, including vaccine supply, the size of the eligible population, weekly vaccination rates, and the interval between vaccine doses set by the Philippine government. Our findings show that prioritizing the pediatric population (aged 5 to 11) for the primary vaccination series within the initial three months is vital. Administering first booster shots to those aged 12 and older during this period is also recommended. Numerical results indicate a preference for highly effective vaccine brands in regions with a high COVID-19 incidence proportion. Conversely, vaccines with lower effectiveness are suitable for primary doses in regions with low COVID-19 cases. This research provides an analytical approach in addressing the complexities of COVID-19 vaccine distribution and allocation. It offers essential guidance to policymakers in the Philippines, enhancing population protection, and contributing to economic recovery.

Baybayin is a pre-Hispanic Philippine writing system used in Luzon island. With the effort in reintroducing the script, in 2018, the Committee on Basic Education and Culture of the Philippine Congress approved House Bill 1022 or the ”National Writing System Act,” which declares the Baybayin script as the Philippines’ national writing system. Since then, Baybayin OCR has become a field of research interest. Numerous works have proposed different techniques in recognizing Baybayin scripts. However, all those studies anchored on the classification and recognition at the character level. In this work, we propose an algorithm that provides the Latin transliteration of a Baybayin word in an image. The proposed system relies on a Baybayin character classifier generated using the Support Vector Machine (SVM). The method involves isolation of each Baybayin character, then classifying each character according to its equivalent syllable in Latin script, and finally concatenate each result to form the transliterated word. The system was tested using a novel dataset of Baybayin word images and achieved a competitive 97.9% recognition accuracy. Based on our review of the literature, this is the first work that recognizes Baybayin scripts at the word level. The proposed system can be used in automated transliterations of Baybayin texts transcribed in old books, tattoos, signage, graphic designs, and documents, among others.

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.