Figure search by text in large scale digital document collections

Yurtsever M. M. E., Ozcan M., Taruz Z., EKEN S., SAYAR A.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, vol.34, no.1, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1002/cpe.6529
  • Journal Name: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Apache Solr, document digitization, Elasticsearch, figure search, full-text search, regular expressions, RETRIEVAL
  • Kocaeli University Affiliated: Yes

Abstract

Digital document collections have been created with the transfer of a large number of documents to digital media. These digital archives have provided many benefits to users. As the diversity and size of digital image collections have grown exponentially, it has become increasingly important and difficult to obtain the desired image from them. The images on the document might contain critical information about the subject of it. In this study, an architecture is developed that can work on large-scale data by creating regular expressions together with full-text search approaches. The performance of the system has been tested on different academic documents and Elasticsearch and Apache Solr insert times are compared. Compared to Elasticsearch, Apache Solr achieved faster and more successful results.