OCR & Document Processing
Multilingual Document Data Extraction
Extracted structured data from PDFs and scanned documents using EasyOCR, Tesseract, and spaCy.
Published
The problem
Important information was locked inside PDFs and scanned documents, often in more than one language, where copy-and-paste and simple text extraction fail. The goal was to turn those documents into structured, usable data.
How I approached it
Prepare the documents
Clean up scans so text recognition starts from the best possible input.
Recognize the text
EasyOCR and Tesseract handle text recognition across languages and layouts.
Extract structured fields
spaCy identifies and pulls out the fields that matter from the recognized text.
Validate before delivery
Extracted values are checked and uncertain results are flagged, so errors are caught before data reaches your systems.
Simplified architecture
- PDFs & scans
- Document preparation
- OCR (EasyOCR, Tesseract)
- NLP extraction (spaCy)
- Validated records
A simplified view of the stages, not a full system diagram.
Tools named
- EasyOCR
- Tesseract
- spaCy
Outcomes
- Structured data from scanned and multilingual documents
- Repeatable extraction in place of manual data entry
- Uncertain values flagged for review