Document Layout Analysis and Classification and Its Application in OCR

Gupta, Gaurav; Niranjan, S. K.; Shrivastava, Ankit; Sinha, R.M.K.

doi:10.1109/edocw.2006.29

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…For example, text in a scanned document may not be easily searchable or editable without additional processing such as optical character recognition (OCR), which converts the image-based text into machine-readable text (Gupta, 2006;Hsu et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Securing Digital Copies of the Documents to Ensure Documents' Integrity

Szyjewski

2023

ERSJ

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Purpose: This article addresses the critical issue of securing the electronic documentation flow, focusing on maintaining document content integrity without compromising ease of use in everyday processes involving documents. Design/Methodology/Approach: The proposed electronic document verification method is versatile and applicable to both electronic and printed documents. The described approach has been meticulously designed and rigorously tested within a certification environment, where the need for confirming users' skills and knowledge is paramount. Findings: In the contemporary landscape dominated by the prevalence of electronic markets and document flow, the potential susceptibility of electronic documentation to forgery often goes unnoticed. The transition to electronic formats renders documentation more vulnerable to unauthorized modifications, leaving no discernible traces of tampering. The advancement of Information Technology (IT) techniques further facilitates the facile alteration of electronic documents, irrespective of whether they are in the form of scanned images stored as bitmap files or in Portable Document Format (PDF). Although commonly perceived as tamper-resistant, these formats are not immune to manual document file manipulations, presenting an avenue for fraudulent activities. Practical Implications: Established techniques such as Private Key Infrastructure (PKI) or cryptographic hash functions for generating document checksums can be employed for this purpose. While effective in preserving content integrity, these methods pose a significant challenge in terms of usability for the average computer user. The study demonstrates the effectiveness of the QR codes usage in simplifying the verification process while maintaining credibility of the document. Originality/value: The article presents a detailed description of the approach, focusing on secure QR codes with extended unique identifiers for efficient verification. It further examines the implementation of this approach within the author's ICT system within International Computer Driving License (ICDL) Certification Program in Poland.

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Section: Literature Reviewmentioning

confidence: 99%

Securing Digital Copies of the Documents to Ensure Documents' Integrity

Szyjewski

2023

ERSJ

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“…Many methods for understanding document images have been investigated to extract and classify meaningful information from documents. Classical methods of layout analysis [1] involve performing morphological operations, connected component analysis, and then classifying extracted features into their constituent regions. Recent advances in highly precise and robust deep neural networks have led to the exploration of deep learning-based layout analysis as well.…”

Section: Introductionmentioning

confidence: 99%

ScreenSeg: On-Device Screenshot Layout Analysis

Goyal¹,

Munjal²,

Moharana³

et al. 2021

Preprint

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We propose a novel end-to-end solution that performs a Hierarchical Layout Analysis of screenshots and document images on resource constrained devices like mobilephones. Our approach segments entities like Grid, Image, Text and Icon blocks occurring in a screenshot. We provide an option for smart editing by auto highlighting these entities for saving or sharing. Further, this multi-level layout analysis of screenshots has many use cases including content extraction, keyword-based image search, style transfer, etc. We have addressed the limitations of known baseline approaches, supported a wide variety of semantically complex screenshots, and developed an approach that is highly optimized for on-device deployment. In addition, we present a novel weighted NMS technique for filtering object proposals. We achieve an average precision of about 0.95 with a latency of around 200ms on the Samsung Galaxy S10 Device for a screenshot of 1080p resolution. The solution pipeline is already commercialized in Samsung Device applications i.e. Samsung Capture, Smart Crop, My Filter in Camera Application, Bixby Touch.

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“…[25] performs horizontal dilation and the length of the line is fixed so that the lines can be extracted completely and heuristic rules are applied for text and non-text region classification. Region growing and analysis with heuristic rule is also applied where the generic modeling of paper layout is known [26]. Connected component generation and heuristics are also applied in [27].…”

Section: Introductionmentioning

confidence: 99%

Text and Non-text Segmentation and Classification from Document Images

İbrahim¹,

Isa

Rajkumar

2008

2008 International Conference on Computer Science and Software Engineering

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Text and non-text segmentation and classification is very important in document layout analysis system before it is presented to an OCR system. Heuristic rules have been used in segmenting and classifying the text and non-text blocks. This research focuses on the classification of non-text block in technical documents into table, graph, and figure.A comparative study is conducted between backpropagation neural network and support vector machine and the result shows that support vector machine classifies better than back propagation neural network.

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Document Layout Analysis and Classification and Its Application in OCR

Cited by 11 publications

References 7 publications

Securing Digital Copies of the Documents to Ensure Documents' Integrity

Securing Digital Copies of the Documents to Ensure Documents' Integrity

ScreenSeg: On-Device Screenshot Layout Analysis

Text and Non-text Segmentation and Classification from Document Images

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