Image Structured Annotation Based on Deep Neural Network Natural Language Processing

Abstract

The image structuring process was mainly divided into three stages: model training, model prediction, and report structuring. In the report structure stage, based on the feature annotation sequence, this paper associated the text sequence with the corresponding table structure and stored the text sequence in the corresponding database in the background. In dataset 1, the accuracy rate of removing visual information submodel was 30 %, and that of removing semantic information submodel was 50 %. The scheme proposed in this paper was to better perform automatic image annotation and meet the requirements of image annotation in the era of Big Data.