Local Convolutional Neural Network Based Pop-Up Text Recognition and Sentiment Analysis

Abstract

Pop-ups are a recently popular way of human-computer interaction, allowing viewers to actively participate in the discussion of a film video, but pop-ups can be deleted due to exceeding the size limit of the pop-up pool, and pop-up data is difficult to obtain in its entirety. To this end, this paper proposes a local convolutional neural network (CNN) based pop-up text sub-recognition algorithm. Finally, the fuzzed data is reconstructed into a data look-up table and the contents of the data look-up table are formatted for output. The results of this text classification and recognition algorithm are compared with those of four commonly used domestic and international search engines by subjective evaluation method, and the proposed text classification and recognition algorithm is found to be advanced.