Differential Analysis of Modern Text Spotting Methods: A Systematic Review

Sheshang Degadwala and Riddhi Dave

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 5, 68-78

Abstract: Text spotting, the task of detecting and recognizing text within images, is vital in applications like document analysis, autonomous navigation, and surveillance. The motivation for this review arises from the growing need for accurate automated text extraction methods, driven by the surge of visual data and the complexity of real-world environments. Despite advances in deep learning and computer vision, current text spotting techniques face significant challenges, including handling complex backgrounds, curved or distorted text, varied font styles, and low-resolution images. These limitations restrict their effectiveness in diverse, real-world settings. This systematic review aims to conduct a differential analysis of modern text spotting methods, highlighting their strengths, weaknesses, and performance in addressing such challenges. The objectives are to evaluate state-of-the-art techniques, identify gaps in the field, and propose future research directions. By critically synthesizing recent literature, this review provides insights that can help enhance the robustness and accuracy of text spotting systems, making them more adaptable to real-world conditions.

Keywords: Text Spotting; Deep Learning; Scene Text Detection; Optical Character Recognition; end-to-end Frameworks; Machine Learning (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410586
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT2410586 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT2410586/CSEIT2410586 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i5:id:293

DOI: 10.32628/CSEIT2410586

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().