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Document Layout Analysis: A Comprehensive Survey

Published:16 October 2019Publication History
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Abstract

Document layout analysis (DLA) is a preprocessing step of document understanding systems. It is responsible for detecting and annotating the physical structure of documents. DLA has several important applications such as document retrieval, content categorization, text recognition, and the like. The objective of DLA is to ease the subsequent analysis/recognition phases by identifying the document-homogeneous blocks and by determining their relationships. The DLA pipeline consists of several phases that could vary among DLA methods, depending on the documents’ layouts and final analysis objectives. In this regard, a universal DLA algorithm that fits all types of document-layouts or that satisfies all analysis objectives has not been developed, yet. In this survey paper, we present a critical study of different document layout analysis techniques. The study highlights the motivational reasons for pursuing DLA and discusses comprehensively the different phases of the DLA algorithms based on a general framework that is formed as an outcome of reviewing the research in the field. The DLA framework consists of preprocessing, layout analysis strategies, post-processing, and performance evaluation phases. Overall, the article delivers an essential baseline for pursuing further research in document layout analysis.

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  1. Document Layout Analysis: A Comprehensive Survey

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                cover image ACM Computing Surveys
                ACM Computing Surveys  Volume 52, Issue 6
                November 2020
                806 pages
                ISSN:0360-0300
                EISSN:1557-7341
                DOI:10.1145/3368196
                • Editor:
                • Sartaj Sahni
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                Publication History

                • Published: 16 October 2019
                • Revised: 1 July 2019
                • Accepted: 1 July 2019
                • Received: 1 July 2018
                Published in csur Volume 52, Issue 6

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