CRNN model for text detection and classification from natural scenes

Abstract

<span lang="EN-US">In the emerging field of computer vision, text recognition in natural settings remains a significant challenge due to variables like font, text size, and background complexity. This study introduces a method focusing on the automatic detection and classification of cursive text in multiple languages: English, Hindi, Tamil, and Kannada using a deep convolutional recurrent neural network (CRNN). The architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for effective spatial and temporal learning. We employed pre-trained CNN models like VGG-16 and ResNet-18 for feature extraction and evaluated their performance. The method outperformed existing techniques, achieving an accuracy of 95.0%, 96.3%, and 96.2% on ICDAR 2015, ICDAR 2017, and a custom dataset (PDT2023), respectively. The findings not only push the boundaries of text detection technology but also offer promising prospects for practical applications.</span>

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

“…The study utilizes the APTOS-2019 dataset [28], known for its diversity in high-resolution retinal images across all five DR stages. With 3,662 images, the dataset presents challenges like data imbalance and variation in image quality due to different photographers and conditions.…”