Transform your Khmer documents into editable text instantly. Our advanced OCR engine preserves formatting with 99% accuracy.
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Why building Khmer OCR is harder than English—and how we solved it.
There is no large, public Khmer OCR dataset with handwriting, layouts, or detailed annotations. Most Khmer documents are offline or locked in images and PDFs, so researchers have very little clean text or labeled examples to train modern models.
Because ready-made datasets don’t exist, we generate our own. Using sources like the Khmer Dictionary 2022, we render paragraphs with random font sizes, line spacing, and padding onto different backgrounds—then automatically create bounding boxes and labels for every word.
Real scans are never perfect, so our synthetic images aren’t either. We add motion blur, JPEG compression, brightness and color shifts, slight rotations, and even “negative” samples in other languages to teach the model to survive noisy cameras, bad scans, and mixed-language pages.
High-quality Khmer text is essential for training large language models, but most existing datasets are tiny or inconsistent. Our OCR pipeline helps generate cleaner, larger corpora so Khmer can be supported by the same advanced AI technologies available in other languages.
This page is part of an ongoing project to improve Khmer OCR performance. We’re experimenting with different models, datasets, and layouts to see what works best for real users. Your uploads and feedback help us understand where the system succeeds—and where we still need to improve.
