An automatic table recognition method for interpretation of tabular data in
document images majorly involves solving two problems of table detection and
table structure recognition. The prior work involved solving both problems
independently using two separate approaches. More recent works signify the use
of deep learning-based solutions while also attempting to design an end to end
solution. In this paper, we present an improved deep learning-based end to end
approach for solving both problems of table detection and structure recognition
using a single Convolution Neural Network (CNN) model. We propose
CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade
mask R-CNN HRNet) based model that detects the regions of tables and recognizes
the structural body cells from the detected tables at the same time. We
evaluate our results on ICDAR 2013, ICDAR 2019 and TableBank public datasets.
We achieved 3rd rank in ICDAR 2019 post-competition results for table detection
while attaining the best accuracy results for the ICDAR 2013 and TableBank
dataset. We also attain the highest accuracy results on the ICDAR 2019 table
structure recognition dataset. Additionally, we demonstrate effective transfer
learning and image augmentation techniques that enable CNNs to achieve very
accurate table detection results. Code and dataset has been made available at:
this https URL

本篇论文提出使用单个深度卷积神经网络（CNN）模型的改进型深度学习端到端方法：CascadeTabNet，用于解决表格检测和结构识别的两个问题，并通过有效的迁移学习和图像增强技术在 ICDAR 2013、ICDAR 2019 和 TableBank 数据集上达到了最佳结果。