International Journal on Science and Technology

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Assessment of GPT 5.5 Model for Data Extraction from Degraded Birth Certificates: A Comparative Analysis of Document Integrity States

Author(s) Prof. Marjon D Senarlo, Prof. Dr. Florence Jean B Talirongan
Country Philippines
Abstract Making the civil registration records digitalized in the Philippines has faced weighty challenges. Tangible documents are vulnerable to ecological damage, wear from handling, and difficulties in just storing in a filing cabinet. Experimentation is being done to address these issues by evaluating the capabilities of GPT-5.5 as a multimodal optical character recognition (OCR) tool. The tool was arranged to be tested on three types of data: Birth Reference Number (BReN), full name, and address are extracted from NSO/PSA birth certificate copies with four categorized conditions: normal, wet, folded, and crumpled. 80 samples were collected from Christ the King College de Maranding, Inc., and divided equally for every sample per condition. Setting up in a controlled manner, samples were processed and measured performance using accuracy, precision, recall, and F1-score. Normal and folded samples were extracted in 100%, 98.4% on wet samples, while crumpled samples achieved 95% across all metrics. To enhance administrative efficiency in both local government and academe tools might be the solution because it outperforms the traditional OCR significantly.
Keywords optical character recognition, multimodal large language model, Philippine civil registry, information retrieval, institutional digitization
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-05-21
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