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.

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.

Baybayin is a Tagalog-language writing system primarily used in the northern Philippines during the pre-Hispanic period. In 2018, the House of Representatives approved House Bill 1022 or the “National Writing System Act,” which declares the Baybayin script as the Philippines’ national writing system. Thus, documents, signages, books, etc. may soon have Baybayin texts. However, the Latin alphabet is still the primary script used in the country. Hence, it is possible that Latin and Baybayin scripts may be found on the same text. In this paper, we present an optical character recognition (OCR) system that identifies Baybayin scripts from Latin in a text image. The preprocessing method applies the conversion of the input image to binary data and calculating the respective bounding box of each word found from the text, where we utilize a modified 𝒌 − means algorithm and MATLAB ocr function, respectively. The classification then involves isolating each word and further segmenting each character’s components. With the aid of a support vector machine (SVM) character classifier, we determine the word’s script by the highest number of characters classified into either Baybayin or Latin. To the best of our knowledge, this is the first system that discriminates, at the block level, the Baybayin script from Latin. The proposed algorithm yields a 93.64% recognition accuracy tested in a novel dataset. The accompanying code of the proposed algorithm and the dataset are made publicly available to make the results of the study reproducible.

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