Vertical Text Detection and Recognition in Natural Scene Images: A Vertical Text Classifier and Detector with Gated Dual Adaptive Attention Mechanism
Keywords:
Vertical Text Classifier and Detector Module, IoU with Inclination Algorithm, Text Detection, Text recognition, GDAAM, Semantic reasoning, Vertical Text, Character awarenessAbstract
This paper proposes a novel approach to improving vertical text detection and recognition in natural scene images by integrating the Vertical Text Classifier and Detector Module (VTCD), which incorporates the IoU with Inclination (IoUI) Algorithm into the Gated Dual Adaptive Attention Mechanism (GDAAM). GDAAM is a unique framework for successful text recognition in demanding settings. The suggested Vertical Text Classifier and Detector Module integration intends to increase the Gated Dual Adaptive Attention Mechanism accuracy and resilience in dealing with vertical text in complicated visual situations. The Gated Dual Adaptive Attention Mechanism encoder accurately localizes text areas in natural scene images. The Vertical Text Classifier and Detector Module are used after localization to fine-tune the bounding boxes and improve vertical text detection. The Vertical Text Classifier and Detector Module's enhanced data is smoothly integrated into the Gated Dual Adaptive Attention Mechanism decoder, impacting fine-grained attention modelling. The model constantly adjusts its attention weights depending on the revised bounding boxes, enabling exact text identification by selectively focusing on key visual and textual signals. In addition to tackling the issues given by irregular text forms and different orientations. The reasoning module uses VTCD’s revised bounding boxes to gather contextual information, while character awareness is improved to handle complicated text layouts and occlusions. The visual-semantic ensemble fusion decoder integrates input from both modalities to provide coherent and contextually consistent text recognition results. Extensive trials on benchmark datasets such as ICDAR 2013, ICDAR 2015, and the VTIG-500 show that the proposed Gated Dual Adaptive Attention Mechanism with Vertical Text Classifier and Detector Module works well. The results indicate higher performance in terms of accuracy and resilience compared to cutting-edge techniques, notably in difficult text recognition tasks. The addition of the Vertical Text Classifier and Detector Module to the Gated Dual Adaptive Attention Mechanism broadens in natural scene images on text recognition, displaying promising results when dealing with vertical text in complex visual conditions.
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