Optimizing Nepali PDF Extraction: A Comparative Study of Parser and OCR Technologies

TL;DR

This study compares PDF parsing and OCR methods for extracting Nepali text from PDFs, finding OCR with PyTesseract offers better accuracy and versatility despite longer processing times.

Contribution

It provides a comparative analysis of parser and OCR technologies specifically for Nepali PDFs, highlighting the strengths and limitations of each approach.

Findings

PDF parsers are faster but less accurate with non-Unicode fonts

OCR with PyTesseract offers consistent accuracy across PDF types

PyTesseract balances speed and accuracy for Nepali PDF extraction

Abstract

This research compares PDF parsing and Optical Character Recognition (OCR) methods for extracting Nepali content from PDFs. PDF parsing offers fast and accurate extraction but faces challenges with non-Unicode Nepali fonts. OCR, specifically PyTesseract, overcomes these challenges, providing versatility for both digital and scanned PDFs. The study reveals that while PDF parsers are faster, their accuracy fluctuates based on PDF types. In contrast, OCRs, with a focus on PyTesseract, demonstrate consistent accuracy at the expense of slightly longer extraction times. Considering the project's emphasis on Nepali PDFs, PyTesseract emerges as the most suitable library, balancing extraction speed and accuracy.