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✨ Refined Text (Gemini)
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Built for CPU-only environments, on-prem deployment, historical documents, and low-resource scripts.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 (common)
- Heavily relies on lexicons / correction
- May "normalize" or alter original spelling
- Struggles with rare words & historical orthography
Vision-first OCR (NextOCR)
- 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- Works better for complex scripts & historical documents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 — not a one-time training event.
Continual learning enables OCR quality to improve as real-world documents are processed, while keeping deployment practical for CPU-only servers.
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 Study
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.
Contact
Get in touch for demos, pricing, or technical discussions.
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Email: danhhong@gmail.com
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Phone: (+855) 95 333 409
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Telegram: t.me/hout18