Document and Text Processing
Zobeir Raisi; Vali Mohammad Nazarzehi
Abstract
The Persian language presents unique challenges for scene text recognition due to its distinctive script. Despite advancements in AI, recognition in non-Latin scripts like Persian still faces difficulties. In this paper, we extend the vanilla transformer architecture to recognize arbitrary shapes of ...
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The Persian language presents unique challenges for scene text recognition due to its distinctive script. Despite advancements in AI, recognition in non-Latin scripts like Persian still faces difficulties. In this paper, we extend the vanilla transformer architecture to recognize arbitrary shapes of Persian text instances. We apply Contextual Position Encoding (CPE) to the baseline transformer architecture to improve the recognition of Persian scripts in wild images, especially for oriented and spaced characters. The CPE utilizes position information to generate contrastive data pairs that help better in capturing Persian characters written in a different direction. Moreover, we evaluate several state-of-the-art deep-learning models using our prepared challenging Persian scene text recognition dataset and develop a transformer-based architecture to enhance recognition accuracy. Our proposed scene text recognition architecture achieves superior word recognition accuracy compared to existing methods on a real-world Persian text dataset.
H.3.2.2. Computer vision
Zobeir Raisi; Valimohammad Nazarzehi; Rasoul Damani; Esmaeil Sarani
Abstract
This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and ...
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This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and five deep-learning models, including Faster-RCNN, SSD, YOLOv3, YOLOv5, and YOLOv9 models using the benchmark INRIA dataset. The experimental results show that although classical methods such as HOG + Gaussian SVM outperform other classical approaches, they are outperformed by deep learning techniques. Furthermore, Classical methods have limitations in detecting partially occluded, distant objects and complex clothing challenges, while recent deep-learning models are more efficient and provide better performance (YOLOv9) on these challenges.
