Computer Vision Based Automatic Margin Computation Model for Digital Document Images

Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve...

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Veröffentlicht in:SN computer science Jg. 4; H. 3; S. 253
Hauptverfasser: Guha, Abhijit, Samanta, Debabrata, Sengar, Sandeep Singh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.05.2023
Springer Nature B.V
Schlagworte:
Automation
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Computer vision
Cyber Security and Privacy in Communication Networks
Data Structures and Information Theory
Digital imaging
Electronic documents
Information retrieval
Information Systems and Communication Service
Localization
Machine learning
Methods
Model accuracy
Morphology
Noise
Original Research
Pattern Recognition and Graphics
Software Engineering/Programming and Operating Systems
Structural members
Typography
Vision
Text localization
Computer vision
Margin detection
Digital image processing
ISSN:2661-8907, 2662-995X, 2661-8907
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Zusammenfassung:Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01693-5