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I will review and clean PDF extraction output into json and markdown
Acerca de este Servicio
Your PDF extraction output looks usable, but you need it cleaned and checked before review, cleanup, schema mapping, or RAG ingestion preparation?
I review existing parser output from Docling, PyMuPDF, Unstructured, or similar tools and create:
- normalized JSON blocks with source file, page number, bounding box, block ID, and provenance
- - a concise quality report that flags missing, noisy, or risky structure
- - clean Markdown with source-reference comments
- - optional JSONL chunk records for Standard or Premium packages
The work starts from your goal: which fields matter, which IDs or source references must be preserved, and how you will use the output downstream.
What I need:
- existing parser JSON or 3-5 sample pages for a quick sample check
- - target output: JSON, Markdown, JSONL chunks, or a specific schema
- - fields, page metadata, source references, or IDs that must stay traceable
What I do not cover:
- OCR accuracy guarantees
- - full RAG chatbot builds
- - legal, medical, or compliance ownership
- - production SaaS deployment
- - scanned document cleanup or complex table reconstruction
- - perfect extraction from arbitrary documents
Tecnología:
Python
FAQ
Which parser formats can you work with?
Docling JSON is the best fit. PyMuPDF, Unstructured, LlamaParse, or similar JSON/dict-style parser output may also work after a quick sample check.
Do you provide OCR or table reconstruction?
Not by default. This gig is for reviewing and cleaning existing parser output. Scanned documents, OCR cleanup, and complex table reconstruction need a custom scope after a sample check.
Is this a RAG system build?
No. I can prepare reviewable JSON, Markdown, or JSONL records for ingestion preparation, but I do not build the chatbot, retrieval system, vector database, or answer-quality evaluation.
