Computer Vision Based Automatic Margin Computation Model for Digital Document Images

Title

Computer Vision Based Automatic Margin Computation Model for Digital Document Images

Creator

Guha A.; Samanta D.; Sengar S.S.

Description

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. 2023, Crown.

Source

SN Computer Science, Vol-4, No. 3

Date

2023-01-01

Publisher

Springer

Subject

Computer vision; Digital image processing; Margin detection; Text localization

Coverage

Guha A., Department of Data Science, CHRIST (Deemed to be) University, Karnataka, Bengaluru, India, R &D Scientist, First American India Private Limited, Karnataka, Bengaluru, India; Samanta D., Department of Data Science, CHRIST (Deemed to be) University, Karnataka, Bengaluru, India, Department of Computational Information Technology, Rochester Institute of Technology, Germia Campus, Prishtina, Kosovo, 10000; Sengar S.S., Department of Computer Science, Cardiff Metropolitan University, Cardiff, CF5 2YB, United Kingdom

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 2662995X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s42979-023-01693-5

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150171922&doi=10.1007%2fs42979-023-01693-5&partnerID=40&md5=153905902498c35e46288fbcddde7c44