PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML

TL;DR

This paper introduces a comprehensive table recognition system for scientific literature, combining structure recognition, text detection, and recognition, achieving high accuracy in the ICDAR 2021 competition.

Contribution

The method integrates customized MASTER-based algorithms and PSENet for robust table structure and text line detection, advancing scientific literature parsing.

Findings

Achieved 96.84% TEDS score on validation data.

Achieved 96.32% TEDS score on final evaluation.

Effective integration of structure and text recognition techniques.

Abstract

This paper presents our solution for ICDAR 2021 competition on scientific literature parsing taskB: table recognition to HTML. In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment.Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm. PSENet [2] is used to detect each text line in the table image. For text linerecognition, our model is also built on MASTER. Finally, in the box assignment phase, we associatedthe text boxes detected by PSENet with the structure item reconstructed by table structure prediction,and fill the recognized content of the text line into the corresponding item. Our proposed methodachieves a 96.84% TEDS score on 9,115 validation samples in the development phase, and a 96.32%TEDS score on 9,064 samples in the final evaluation phase.