ocr-document-processor
Extract text and structure from scans, images, and scanned PDFs. Use for OCR, searchable PDFs, table extraction, receipt parsing, and business card parsing.
git clone --depth 1 https://github.com/dkyazzentwatwa/chatgpt-skills /tmp/ocr-document-processor && cp -r /tmp/ocr-document-processor/ocr-document-processor ~/.claude/skills/ocr-document-processorSKILL.md
# OCR Document Processor Handle OCR-heavy inputs where text must be recovered from images or scanned pages. ## Use This For - OCR on images and scanned PDFs - Searchable PDF export - Structured extraction to text, markdown, JSON, or HTML - Table extraction from scanned material - Receipt parsing and business card parsing ## Workflow 1. Decide whether plain OCR, structured extraction, or document-specific parsing is needed. 2. Preprocess noisy inputs before extraction when skew, blur, or shadows are present. 3. Use `scripts/ocr_processor.py` for core OCR tasks. 4. Use the focused helpers when the input is specialized: - `scripts/business_card_scanner.py` - `scripts/receipt_scanner.py` 5. Return confidence caveats when the source is low quality, rotated, handwritten, or multilingual. ## Guardrails - Prefer explicit language selection when accuracy matters. - Do not claim fields are exact when OCR confidence is weak. - Route non-scanned digital PDFs to `document-converter-suite` instead of OCR by default.
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