Video image-text courseware text extraction method and device, equipment and medium

Abstract

The invention relates to artificial intelligence, and discloses a video image-text courseware text extraction method. The method comprises the steps of classifying video image-text courseware, identifying pictures in the video image-text courseware and all textboxes existing on the pictures by adopting a text identification method, and obtaining structured information of the pictures; performing regularization processing on the picture to update the coordinate of each textbox on the picture; constructing a neural network language model for training, wherein the input of the neural network language model is a plurality of character fields, and the output of the neural network language model is the splicing rationality of the plurality of character fields; splicing the textboxes, performingreasonability judgment on splicing by adopting a trained neural network language model, and if the reasonability conforms to a preset value, extracting texts after the textboxes are spliced. In addition, the invention also relates to a blockchain technology, and the video image-text courseware can be stored in the blockchain. According to the invention, complete readable, processable and structured texts can be provided from images and videos.

Description

technical field [0001] The invention relates to artificial intelligence, in particular to a method, device, electronic equipment and computer-readable storage medium for extracting text from video, graphic and text courseware. Background technique [0002] With the rapid development of artificial intelligence technology and mobile networks, online education has become more and more popular. The teaching of online education is usually carried out in the form of video, ppt, or graphic html, and there is a large amount of course knowledge stored in the form of pictures. However, effective, accurate and structured textual information is often required to make use of this knowledge. Therefore, there is an urgent need for structured text extraction of these video graphic data. [0003] In the prior art, the text extraction of videos and pictures is based on OCR (Optical Character Recognition, Optical Character Recognition), but no matter what OCR recognition algorithm is, it is ...

AI Technical Summary

Problems solved by technology

In addition, video files and ppt files used for recognition usually contain a large amount of non-standard text, such as oblique text, vertical text, and animation effects will also cause the failure of recognition

Therefore, although the existing OCR technology can recognize the text contained in video, ppt, and graphics, the extracted text is fragmented, incomplete, and text fragments lacking structural information, and cannot be further extracted and used

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