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Vision-first OCR for Complex & Multilingual Documents
NextOCR recognizes text directly from visual signals — without relying on dictionaries or language-model post-correction.
Built for CPU-only environments, on-prem deployment, historical documents, and low-resource scripts.
Why Vision-first OCR?
Many OCR systems are language-first: they depend on dictionaries, spell-checking, or large language models to "fix" recognition. This can distort original spelling and fails on historical variants, names, and domain-specific terms.
Language-first OCR
- Heavily relies on lexicons / correction
- May "normalize" or alter original spelling
- Struggles with rare words & historical orthography
Vision-first OCR
- Recognizes characters as they appear in the image
- Preserves original spelling and structure
- Works better for complex scripts & historical documents
Especially important for Khmer and other scripts with high orthographic variation, including historical and manuscript sources.
Continual Learning by Design
NextOCR is built for continual learning: it adapts to new layouts, fonts, document types, and writing styles over time.
Multilingual Training Roadmap
Khmer is the core focus. NextOCR is designed to expand into more languages within one vision-first framework.
Other languages are actively being trained and evaluated.
Use Cases
Case Studies
Vision-first vs. Traditional OCR on 1950s Khmer Texts
We ran both systems on a pre-standardization Khmer patriotic song from the 1950s. The language-first system made 20 errors by imposing modern orthography; NextOCR made 1.
Vision-first vs. Multimodal LLM on the 1930s Khmer Tripitaka
On the royal preface to the Khmer Tripitaka (Buddhist Institute, 1930s), a leading multimodal LLM made 8 errors — then invented a scholarly excuse for them. NextOCR made 0.
Contact
Reach out for demos, pricing, or technical discussions.
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EMAILdanhhong@gmail.com
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PHONE(+855) 95 333 409
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TELEGRAMt.me/hout18