Cloud Computer Research on Table Detection Model Based on the DC-LSTM Model

Abstract

In view of the fact that tables are not easy to detect, this paper designs a table detection model based on the DC-LSTM module, which references the Convolutional Long Short-Term Memory (ConvLSTM). The model uses the backbone network of target detection to extract convolution features. The feature pyramid network is used to complete the detection task, and the DC-LSTM module is embedded in a special position in the feature pyramid network. In order to evaluate the performance of the DC-LSTM module, we added the DC-LSTM module to the YOLO v3, SSD network. Specifically, we added the DC-LSTM module to the YOLO v3 network, the new model can achieve an accuracy of more than 98% on the Table Bank data set and own data set. The model in this paper can realize the automatic extraction of table document images, which is of great significance for the realization of automated data collection.