EXTRACTING TEXTUAL INFORMATION FROM BRIDGE INSPECTION RECORD USING DEEP LEARNING

Abstract

A large number of inspection records have been compiled to date as records of bridge inspections. However, most of them are in paper form or the data is saved in PDF format, which makes it difficult to utilize the accumulated data. If it is possible to build a database of inspection records by automatically extracting data from inspection records, it will be possible to analyze data based on a huge number of inspection records. Moreover, the element numbers, which are important information to know the location of damaged members, etc., can be found in the bridge drawing. However, a lot of the element numbers overlap with lines of the structural members, making it difficult to extract them by general OCR processing. In this study, the element numbers listed on the bridge drawings in the inspection record were extracted utilizing object detection by deep learning. As a result, it was confirmed that the extraction of the element numbers that overlapped with the lines was possible with high accuracy.