Hybrid deep neural network with adaptive galactic swarm optimization for text extraction from scene images

Text obtained in natural scenes contains various information; therefore, it is extensively used in various applications to understand the image scenarios and also to retrieve the visual information. The semantic information provided by this scene image is very much valuable for human beings to realize the whole environment. But the text in such natural images depicts a flexible appearance in an unconstrained environment which makes the text identification and character recognition process a more challenging one. Therefore, a weighted naïve Bayes classifier (WNBC)-based deep learning process is used in this framework to effectively detect the text and to recognize the character from the scene images. Normally, the natural scene images may carry some kind of noise in it, and to remove that, the guided image filter is introduced at the pre-processing stage. The features that are useful for the classification process are extracted using the Gabor transform and stroke width transform techniques. Finally, with these extracted features, the text detection and character recognition is successfully achieved by WNBC and deep neural network-based adaptive galactic swarm optimization. Then, the performance metrics such as accuracy, F1-score, precision, mean absolute error, mean square error and recall metrics are evaluated to estimate the adeptness of the proposed method.