Intelligent recognition and analysis of concrete material text based on image data

Abstract

During the construction of concrete dams, a large amount of material information expressed in unstructured text is generated, which is of great significance for engineering quality inspection and further research and development of materials. Due to the limitations of data management technology, there is a large amount of material text data stored in the form of images, which is difficult to directly edit and utilize, and cannot meet the needs of intelligent analysis and management of concrete dam material data. In addition, there is currently a lack of intelligent information extraction mechanisms for massive material text data, making it difficult to efficiently obtain key information from the text. An intelligent interpretation method was proposed for concrete material text based on image data, which identifies text information in image data and improves the detection and recognition efficiency of inclined material text. Based on the interpreted image data, starting from the multi perspective text feature relationship, using MMR algorithm as the framework, combined with BERT model and TF-IDF algorithm, considering the importance of text semantics and professional terminology, a set of intelligent analysis technology for concrete material text was established to extract key information from concrete material text. Based on actual concrete material text, the accuracy of extracting keywords using this method is 86.67%, which is superior to other commonly used keyword extraction models. Research findings provide a new method for processing non editable text data of concrete materials, which helps to improve the intelligent management level of concrete dam material data.

Keywords

concrete dam / material data / text detection / intelligent recognition / key information