A Text Detection and Recognition Algorithm for English Teaching Based on Deep Learning

Traditional English teaching cannot make effective use of various resources, and the scheduling ability is poor. People cannot accurately obtain the information in the English textbook text in the learning process, resulting in some people who cannot better learn and master the English language. For this problem, this study adopts deep learning algorithm and establishes an English teaching text algorithm based on association semantic rules to mine the features between sentences and phrases in the text provided by English teachers. The proposed algorithm completes the feature extraction of the English teaching text and also analyzes the association analysis between semantics in English teaching text. In fact, its essence is to get English teaching association rules on the basis of information theory. By combining with semantic similarity information, English teaching text can be accurately detected and identified. The simulation results show that the proposed algorithm can accurately extract English teaching text information, and the accuracy and convergence speed during extraction are higher than other competing algorithms.