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

arXiv: https://arxiv.org/abs/2407.04577 · HTML: https://mlsanity.com/paper/2407.04577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine](https://mlsanity.com/paper/2505.01435/) (2025)
- [NepKANUN: A RAG-Based Nepali Legal Assistant](https://mlsanity.com/paper/2609.15999/) (2026)

## Abstract (arXiv)

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.