The Research And Implementation Of Table Recognition System Based On Deep Learning

With the popularization and development of information technology and computer technology,more and more enterprises choose information system to improve enterprise management and business processes,enhance enterprise efficiency and competitiveness.How to help enterprises realize informatization is a subject worthy studying.This paper takes the automatic processing of document information in the process of enterprise information system development as the research material,researches and realizes a table recognition system based on deep learning.Table recognition system plays an important role in enterprise informatization.Firstly,it can help enterprises automatically handle a large amount of table data,such as sales reports,purchase orders,financial statements,etc.Traditional manual processing of table data requires a lot of manpower and time costs,and is prone to errors and omissions.Table recognition system can automate these tasks and improve the efficiency and accuracy of enterprise information processing.Secondly,it can also provide better data visualization and analysis capabilities to help enterprises better understand and use data.By converting table data into a processable digital format,enterprises can use data analysis tools to conduct more in-depth analysis and data mining,it can find more business opportunities and values.In order to implement a table recognition system,this paper using purchase order table data proposes a table recognition method based on deep learning,which can extract and recognize table structure and text information in the form document more accurately.This method uses object detection and instance segmentation model to detect and extract table areas in the document,uses semantic segmentation model to extract table lines in the table areas and reconstruct table structure,then uses optical character recognition technology to detect and recognize text in the document.The main work of this paper is as follows:First,this paper proposes a three-stage table recognition algorithm,which divides table recognition into three stages:table area detection,table structure extraction,table text detection and recognition.At each stage of table recognition,functional modules have been improved and optimized according to actual needs of the project.Then based on actual table document data set,many experiments have been carried out to analyze the defects of this algorithm,verify accuracy and efficiency of this algorithm,those provide an important algorithm support for the design and implementation of the table recognition system.Secondly,based on text image super-resolution processing algorithm to modify text detection and recognition module in the system,perform super-resolution processing on the text boxes which extracted from the text detection,so as to generate a high-resolution text image for text recognition.Then use financial table data to fine-tune text detection model and text recognition model,improve the accuracy of text detection and recognition in the table recognition system.In order to enhance the reusability of this part,this paper combines text detection,text image super-resolution processing and text recognition into a new end-to-end OCR module.Finally,this paper designs and implements a table recognition system,include uploading table images and correcting table recognition results.This table recognition system has been applied to an enterprise information system and achieves a good application result.