Deep learning-based modified-EAST scene text detector: insights from a novel multiscript dataset

Abstract

The field of computer vision has seen significant transformation with the emergence and advancement of deep learning models. Deep learning waves have a significant impact on scene text detection, a vital and active area in computer vision. Numerous scientific, industrial, and academic procedures make use of text analysis. Natural scene text detection is more difficult than document image text detection owing to variations in font, size, style, brightness, etc. The National Institute of Technology Jalandhar-Text Detection dataset (NITJ-TD) is a new dataset that we have put forward in this study for various text analysis tasks including text detection, text segmentation, script identification, text recognition, etc. a deep learning model that seeks to identify the text’s location within the image,which are gathered in an unrestricted setting. The system consists of an NMS to choose the best match and prevent repeated predictions, and a modified EAST to pinpoint the exact ROI in the image. To improve the model’s performance, an enhancement module is added to the fundamental Efficient and Accurate Scene Text detector (EAST). The suggested approach is contrasted in terms of text word detection in the image. Several pre-trained models are used to assign the text word to various intersections over Union (IoU) values. We made use of our NITJ-TD dataset, which is made up of 1500 photos that were gathered from various North Indian sites. Punjabi, English, and Hindi scripts can be seen on the images. We also examined the outcomes of the ICDAR-2013 benchmark dataset. On both the suggested dataset and the benchmarked dataset, our approach performed better.