Digital Image Retrieval Based on Selective Conceptual Based Features for Important Documents

Ghadekar, Premanand; Kaneri, Sushmita; Undre, Adarsh; Jagtap, Atul

doi:10.1007/978-981-15-5258-8_53

Cited by 2 publications

(1 citation statement)

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“…Advances in internet technology, creativity, innovation and the tremendous increase in digital image creation in documents have given rise to the use of techniques to detect and query images on the web. Content-based image retrieval systems are well studied and a number of researchers have proposed a number of techniques [1], [2], [3], [4]. Some of the authors proposed a new computational backend model that includes a dataset and OCR services for Arabic document information retrieval (ADIR) [1], multimodal features for similarity search we apply re-ranking according to averaged or maximum scores [2] and diagram image retrieval and analysis, with an emphasis on methods using contentbased image retrieval (CBIR), textures, shapes, topology and geometry [5].…”

Section: Introductionmentioning

confidence: 99%

Content-based Image Retrieval using Tesseract OCR Engine and Levenshtein Algorithm

Adjetey¹,

Adu-Manu²

2021

IJACSA

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Image Retrieval Systems (IRSs) are applications that allow one to retrieve images saved at any location on a network. Most IRSs make use of reverse lookup to find images stored on the network based on image properties such as size, filename, title, color, texture, shape, and description. This paper provides a technique for obtaining full image document given that the user has some portions of the document under search. To demonstrate the reliability of the proposed technique, we designed a system to implement the algorithm. A combination of Optical Character Recognition (OCR) engine and an improved textmatching algorithm was used in the system implementation. The Tesseract OCR engine and Levenshtein Algorithm was integrated to perform the image search. The extracted text is compared to the text stored in the database. For example, a query result is returned when a significant ratio of 0.15 and above is obtained. The results showed a 100% successful retrieval of the appropriate file base on the match even when partial query images were submitted.

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