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A Survey of Deep Learning Approaches for OCR and Document Understanding

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arxiv 2011.13534 v2 pith:Y7MSZMCS submitted 2020-11-27 cs.CL cs.CVcs.IRcs.LG

Nishant Subramani , Alexandre Matton , Malcolm Greaves , Adrian Lam This is my paper · ORCID
classification cs.CLcs.CVcs.IRcs.LG
keywords understandingdocumentdocumentsmanydeepfieldslearningsurvey
verification ladder T0 review T1 audit T2 compute T3 formal
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Documents are a core part of many businesses in many fields such as law, finance, and technology among others. Automatic understanding of documents such as invoices, contracts, and resumes is lucrative, opening up many new avenues of business. The fields of natural language processing and computer vision have seen tremendous progress through the development of deep learning such that these methods have started to become infused in contemporary document understanding systems. In this survey paper, we review different techniques for document understanding for documents written in English and consolidate methodologies present in literature to act as a jumping-off point for researchers exploring this area.

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Cited by 4 Pith papers

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    The paper systematises India's EV compliance-document lifecycle into a two-layer evidence model, a six-stage lifecycle with four failure loci, an exergy-destruction analytic lens, and a six-problem research agenda.

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    A pipeline classifies 3965 real-estate questionnaires and extracts 35 structured attributes from 2781 selectable-text documents via DeepSeek R1, reporting Jaccard consistency 0.82.

  4. Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing

    cs.CL 2025-08 conditional novelty 4.0 of 10

    Across three invoice datasets, multimodal LLMs extract fields more accurately from raw images than from markdown converted by a parsing tool, with Gemini 2.5 Pro leading.

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