# Digitization of Document and Information Extraction using OCR

arXiv: https://arxiv.org/abs/2506.11156 · HTML: https://mlsanity.com/paper/2506.11156/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning](https://mlsanity.com/paper/1910.09433/) (2019)
- [Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images](https://mlsanity.com/paper/2212.05525/) (2022)
- [A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing](https://mlsanity.com/paper/2307.04245/) (2023)
- [TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal Domain](https://mlsanity.com/paper/2306.02142/) (2023)
- [PILOT: A Promptable Interleaved Layout-aware OCR Transformer](https://mlsanity.com/paper/2504.03621/) (2025)
- [Handwritten Text Recognition Using Convolutional Neural Network](https://mlsanity.com/paper/2307.05396/) (2023)

## Built on by (most cited first, 3 of 3)

- [OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models](https://mlsanity.com/paper/2601.21639/) (2026)
- [From Chaos to Clarity: Schema-Constrained AI for Auditable Biomedical Evidence Extraction from Full-Text PDFs](https://mlsanity.com/paper/2601.14267/) (2025)
- [Structured Layout Priors for Robust Out-of-Distribution Visual Document Understanding](https://mlsanity.com/paper/2605.19866/) (2026)

## Abstract (arXiv)

Retrieving accurate details from documents is a crucial task, especially when handling a combination of scanned images and native digital formats. This document presents a combined framework for text extraction that merges Optical Character Recognition (OCR) techniques with Large Language Models (LLMs) to deliver structured outputs enriched by contextual understanding and confidence indicators. Scanned files are processed using OCR engines, while digital files are interpreted through layout-aware libraries. The extracted raw text is subsequently analyzed by an LLM to identify key-value pairs and resolve ambiguities. A comparative analysis of different OCR tools is presented to evaluate their effectiveness concerning accuracy, layout recognition, and processing speed. The approach demonstrates significant improvements over traditional rule-based and template-based methods, offering enhanced flexibility and semantic precision across different document categories