Table detection method based on feature pyramid network with faster R-CNN
Yawen Liu, Yinghui Jin, Chen‐Chao Huang, Wenzhi Bao · 2020
Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. This paper uses Faster R-CNN with the feature pyramid structure as the main network structure to detect the table.It presents a deep learning-based solution for table detection in document images. In order to adapt to different shapes of tables, we classify the tables and preprocess the text with Run Smooth Length algorithm and open source OCR tools. In contrast to most existed table detection methods that only applicable to PDFs, our method can detect document images, which also applies to PDF (because PDF format can be automatically converted to pictures). To evaluate the effectiveness of our method, we tested on ICDAR, UNLV and TableBank datasets, and achieves F1-measure of 92.59% in UNLV, which is higher than some previous methods.