OCR accuracy is decided before the model ever runs. Skewed scans, stamps over text, and low-DPI faxes wreck recognition, so the pipeline starts with deskewing, denoising, and contrast normalization. A few cheap preprocessing steps often move accuracy more than swapping the recognition engine does.
Thai stacks vowels and tone marks above and below the base character, so engines tuned for Latin scripts drop or misplace them. We use models trained on Thai and validate against documents with dense diacritics, because a tone mark lost in OCR can silently change a word and corrupt downstream search. Tables and multi-column government layouts add another layer the pipeline has to segment correctly.
Raw OCR text is not the goal — searchable, structured content is. We feed the extracted text through the same Thai word segmentation used elsewhere, attach it to the document as a hidden searchable layer, and auto-tag by detected entities. The scanned original stays untouched for legal fidelity while the text layer makes it findable.