An Empirical Review for Character Recognition in Video Frames using Text Detection Algorithms

Abstract

The characters in the text image or video is having more information for recovery and indexing applications. The recognition of characters from the video text images is a difficult task due to the complex backgrounds, grey-scale values, illuminations, and sizes. Hence, various text detection algorithms like feature extracting algorithms including texture and region-based, machine learning, and deep learning approaches are implemented. Optical Character Recognition (OCR) is implemented in detecting the characters from the text frames and gives the text information including the location of the frame for text in the video. The Natural Language Processing (NLP) technique results in high performance in detecting the different text languages from the video frames. Convolutional Neural Network (CNN) algorithms are used in the feature selection process for efficient recognition of text characters from the images. The deep learning algorithms are used in textual regions in the frames of the video for detecting the text. The LSTM combined CNN networks are also implemented for extracting the features from the image for efficient and high-quality output text.