For engineers building document pipelines in Southeast Asia, deploying a global Optical Character Recognition (OCR) model often feels like fitting a square peg into a multilingual round hole. You feed a regional invoice into a modern AI system, expecting cleanly structured data. Instead, the extraction breaks down on Thai tonal marks, misinterprets mixed English-Bahasa Indonesia layouts, or scrambles Vietnamese diacritics. While global OCR platforms handle standard English documents well, they frequently struggle with the systemic complexities of Southeast Asian languages. In healthcare, misread patient intake forms create data bottlenecks that require manual review. In edtech, digitizing regional study materials demands heavy human intervention to correct extraction errors. Even in B2B SaaS platforms, automated expense tracking stalls when confronted with complex, multilingual receipts.…