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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Hanji, Geeta
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Computer Science & IT Engineering

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Enhanced scene text recognition using deep learning based hybrid attention recognition network Patil, Ratnamala S; Hanji, Geeta; Huded, Rakesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4927-4938

Abstract

The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this research, is intended to greatly improve efficiency and accuracy of text recognition in complicated scene situations. HARN makes use of cutting-edge elements including alignment-free sequence-to-sequence (AFS) module, creative attention mechanisms, and hybrid architecture that blends attention models with convolutional neural networks (CNNs). Thanks to its novel attention processes, HARN is capable of comprehending wide range of scene text components by capturing both local and global context information. Through faster network convergence, shorter training times, and better utilization of computing resources, the suggested technique raises bar for state-of-the-art. HARN’s versatility makes it a good choice for range of scene text recognition applications, including multilingual text analysis and data extraction. Extensive tests are conducted to assess the effectiveness of HARN approach and demonstrate it is ability to greatly influence real-world applications where accurate and efficient text recognition is essential.
Adaptive deformable feature augmentation and refinement network for scene text detection and recognition S. Patil, Ratnamala; Hanji, Geeta; Hudud, Rakesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp831-840

Abstract

Scene text recognition (STR) is the task of detecting and identifying text within images captured from natural scenes, a challenging process due to variations in text appearance, orientation, and background complexity. The proposed methodology, adaptive deformable feature augmentation and refinement network (ADFARN), is designed to address these challenges by combining deformable convolutional networks for robust enhanced feature extraction with a novel deep feature refinement (FRE) that leverages refinement for precise text localization. This approach enhances the differentiation between text and background, significantly improving recognition accuracy. The ADFARN methodology includes a comprehensive process of feature extraction, deep feature augmentation module (DFAM), and the generation of score and threshold maps through differentiable binarization. The adaptive nature of the model allows it to handle low resolution and partially occluded text effectively, further increasing its robustness. Additionally, the proposed method aligns visual and textual features seamlessly. Extensive performance evaluation on the common objects in context (COCO)-Text dataset demonstrates that ADFARN outperforms existing state-of-the-art methods in terms of precision, recall, and F1-scores, establishing it as a highly effective solution for STR in real world applications.
Co-Authors Huded, Rakesh Hudud, Rakesh Patil, Ratnamala S S. Patil, Ratnamala