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Enterprise ClientGovernment

Handwritten Cyrillic OCR

92% accuracy on handwritten Cyrillic recognition

Handwritten Cyrillic OCR

Challenge

Handwritten Cyrillic document recognition is a notoriously difficult problem. Existing commercial OCR tools failed below 60% accuracy on the client's document types.

Solution

Custom ML model trained specifically for handwritten Cyrillic recognition, with a document preprocessing pipeline and integration into downstream business systems.

Technical implementation

Custom training dataset, data augmentation pipeline, ensemble model architecture, document layout analysis, preprocessing for noise reduction, and API integration layer.

Outcome

92% accuracy on handwritten Cyrillic — industry-leading for this script.

Business impact

Automated document processing that was previously 100% manual. Reduced processing time from days to minutes per batch.

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