I'm building a document parser for scanned forms and need the easiest stack

If you want the easiest stack for parsing scanned forms, I’d recommend a very pragmatic OCR + layout + extraction setup:

Easiest stack

1) OCR

Pick one of:

• Google Document AI
• AWS Textract
• Azure Form Recognizer

These are the easiest because they handle:

• scanned PDFs / images
• text extraction
• tables / key-value pairs
• some form structure out of the box

If you want the simplest developer experience, Google Document AI or AWS Textract are usually the fastest to production.

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2) Preprocessing

Use:

• OpenCV
• Pillow

For:

• deskewing
• denoising
• thresholding
• crop/rotate fixes

This matters a lot for bad scans.

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3) Parsing / field extraction

If forms are consistent:

• use template-based extraction
• map coordinates/regions per form type

If forms vary:

• use OCR output + LLM post-processing
• or a document AI service with key-value extraction

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4) Backend

Simplest:

• Python + FastAPI

Why:

• easy integration with OCR APIs
• great for PDF/image handling
• easy async job processing

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5) Storage / queue

If you need production-ish scaling:

• S3 / GCS / Azure Blob for files
• Celery + Redis for async processing

For a smaller setup, you can skip Celery at first.

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Recommended “easy mode” stack

Best simple stack

• Python
• FastAPI
• OpenCV + Pillow
• AWS Textract or Google Document AI
• PostgreSQL for extracted data
• S3/GCS for documents

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If you want cheapest/easiest to prototype

• Tesseract OCR
• Python
• OpenCV
• FastAPI

This is cheaper, but usually worse on:

• handwriting
• poor scans
• tables
• form structure

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My practical recommendation

• If you need results fast: AWS Textract + Python/FastAPI
• If you want best “document understanding” experience: Google Document AI + Python/FastAPI
• If forms are fixed templates and cost matters: OpenCV + Tesseract + template mapping

If you want, I can give you:

1. a minimal architecture diagram, or
2. a starter repo structure for the easiest stack.