Deep Learning-Based Text Identification from Hazy Images: A Self-Collected Dataset Approach

This research suggests a deep learning-based method for text identification from hazy images using a self-collected dataset. The problem of identifying text from hazy images is challenging due to the degradation of the image quality caused by various atmospheric conditions. To address this issue, the proposed approach utilizes a deep learning framework that comprises a hybrid architecture wherein a convolutional neural network (CNN) is employed for feature extraction and a recurrent neural network (RNN) is utilized for sequence modelling. A self-collected dataset is employed for training and validation of the proposed approach, which contains hazy images of various text sizes and fonts. The experimental findings show that the suggested technique outperforms state-of-the-art approaches in correctly recognizing text from hazy images. Additionally, the proposed self-collected dataset is publicly available, providing a valuable resource for future investigations in the field. Overall, the proposed approach has potential applications in various domains, including image restoration, text recognition, and intelligent transportation systems. The performance of the trained model is then evaluated using a third-party dataset consisting of blurry photos. The effectiveness of the model may be evaluated using standard metrics, including accuracy, precision, recall, and F1-score.