Curved texts pose a significant challenge in detection in ‘the wild’, primarily due to the inherent variabilities in text orientation and possible distortions while the images are being acquired. Standard text detection models have been observed to exhibit low accuracy in detecting texts on curved surfaces. To fill this gap, there have been a variety of deep learning-based models proposed to date which have achieved low to moderate success. This paper implements a DL-based model for the detection and recognition of text from scenic images. The proposed approach applies different image processing techniques such as Gray scale conversion, noise removal using median filter, normalization and Otsu’s Binarization and a panoptic segmentation technique for achieving desired text detection performance. A synthetic dataset is created which is used to fill in the gaps of character annotation and multi-orientation. The performance of the proposed approach is determined using different evaluation metrics and the results are compared against existing techniques such as You Only Look Once version 5 (YOLOv5), HDBNet, Bidirectional Perspective Network (BiP-Net), and Res18-LVT. Results show that the proposed approach achieves better performance in terms of precision (98.7%), recall (91.7%), and F1 score (94.5%) as compared to existing classification models.

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