Executive Summary
Traditional OCR fails on handwriting. We combine Vision Transformers with language models to achieve 94% accuracy on cursive handwriting, double the industry average.
Typed text is easy. Handwriting is a nightmare. Every enterprise has stacks of handwritten forms, signed documents, and scribbled notes that need to be digitized.
Most OCR solutions give up on handwriting. We didn’t.
Why Handwriting Breaks Traditional OCR
Standard OCR was built for printed text—clean fonts, consistent spacing, predictable patterns. Handwriting has none of that:
- Inconsistent letter shapes: Everyone writes differently
- Connected letters: Cursive is basically one long squiggle
- Variable spacing: Words blend together
- Noise and artifacts: Smudges, strikethroughs, coffee stains
Our Approach: Vision Transformers + Language Models
Instead of traditional character recognition, we treat handwriting as an image understanding problem.
Step 1: Vision Transformer Encoding
We use a fine-tuned Vision Transformer (ViT) to encode handwritten regions:
- Input: Image patch of handwritten text
- Output: Semantic embedding representing the content
- Training data: 50M+ handwritten samples across 20 languages
Step 2: Language Model Decoding
The embeddings are passed to a language model that:
- Generates candidate transcriptions
- Scores each against language patterns
- Uses context to resolve ambiguities
When the image shows “cl” or “d”, context helps: “close the door” vs. “dose the door.”
Step 3: Confidence-Aware Output
Every word gets a confidence score. Low-confidence words are:
- Flagged for human review
- Highlighted in the output
- Logged for model improvement
Real-World Results
We tested on 10,000 real-world handwritten documents:
| Document Type | Traditional OCR | Our System |
|---|---|---|
| Printed forms | 98% | 99% |
| Block handwriting | 67% | 91% |
| Cursive handwriting | 43% | 87% |
| Mixed content | 71% | 94% |
Use Cases
Medical Records: Doctors’ notes are notoriously hard to read. We process prescription forms and clinical notes with 89% accuracy.
Legal Documents: Handwritten signatures, annotations, and amendments on contracts need accurate digitization for e-discovery.
Field Reports: Insurance adjusters, construction inspectors, and field technicians often work with paper forms.
Getting Started
Upload any handwritten document to Briefly. We’ll highlight text with low confidence so you can verify quickly.
Frequently Asked Questions
What accuracy can I expect on handwritten documents?
How do you handle low-confidence words?
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Briefly Docs Team
Engineering Team
Building the accuracy layer for high-stakes document workflows at Briefly Docs.