Zobeir Raisi - Electrical Engineering Department, Chabahar Maritime University, Chabahar, Iran.
Vali Mohammad Nazarzehi - Electrical Engineering Department, Chabahar Maritime University, Chabahar, Iran.

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.

Scene Text Recognition, Persian Scripts, Contextual Position Encoding, Transformers, deep learning