Title: ORNAIC-TDNSI: optimal RetinaNet with artificial immune classification for text detection on natural scene images

Authors: Sharfuddin Waseem Mohammed; Brindha Murugan

Addresses: Department of CSE, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of CSE, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

Abstract: Text detection and recognition from natural scene images is helpful in many industrial, surveillance, and security applications. Text detection in natural scenes is a vital but challenging issue due to differences in line orientation, text fonts and size. This study introduces an optimal RetinaNet with artificial immune classification for text detection on natural scene images (ORNAIC-TDNSI). The ORNAIC-TDNSI model encompasses two major processes namely textual region detection and text recognition from detected regions. At the initial stage, the RetinaNet object detector is applied for detection of textual regions in the natural scene images. For enhancing the detection efficiency of the RetinaNet model, group teaching optimisation algorithm (GTOA) is utilised. Next, artificial immune classification (AIC) model is applied for accurate text recognition. The experimental validation of the ORNAIC-TDNSI model is tested on ICDAR-2015, ICDAR-2017, and Total-Text datasets. The comparison study reported that the ORNAIC-TRNSI model outperforms the other DL models.

Keywords: natural scene images; text recognition; deep learning; RetinaNet; artificial immune classification; AIC.

DOI: 10.1504/IJCSE.2024.142832

International Journal of Computational Science and Engineering, 2024 Vol.27 No.6, pp.663 - 679

Received: 02 Jan 2023
Accepted: 25 Aug 2023
Published online: 28 Nov 2024 *

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