Our current parser misses handwritten fields and signatures

A good way to address this is to treat handwritten fields and signatures as separate extraction targets from printed text.

Why the current parser misses them

Most standard OCR/parsing pipelines are optimized for:

• machine-printed text
• clean layout detection
• explicit text zones

Handwriting and signatures usually fail because they require:

• handwriting OCR / HTR rather than standard OCR
• field-specific detection for form regions
• signature detection/classification instead of text extraction

Recommended approach

1. Detect the form fields first

   • Use template matching or document layout models to locate expected handwritten areas.
   • If the form is semi-structured, map field coordinates per template.

2. Use handwriting-aware OCR

   • Run a specialized model on handwritten regions only.
   • Examples: TrOCR, Kraken, Azure Form Recognizer, Google Document AI, AWS Textract, etc.

3. Handle signatures separately

   • Signatures are often not meant to be read as text.
   • Use a signature detector to classify whether a signature is present.
   • Optionally extract the signature image for verification or archival.

4. Post-process by field type

   • For name/date/notes fields: normalize handwriting OCR output.
   • For signature fields: return present/absent, plus bounding box or cropped image.

Practical pipeline

• Document preprocessing
• Layout detection / form field localization
• Printed text OCR
• Handwritten field OCR
• Signature presence detection
• Merge results into final structured output

If you want to fix the parser quickly

The fastest improvement is usually:

• add a handwriting OCR pass on known blank-fill regions
• add a signature detector for signature boxes/lines

If you’d like, I can help you with either:

• a technical design for updating the parser, or
• sample code/pseudocode for handwriting and signature detection.