Synthesizing Realistic Data for Table Recognition
Qiyu Hou Jun Wang Meixuan Qiao Lujun Tian
To overcome the limitations and challenges of current automatic table data
annotation methods and random table data synthesis approaches, we propose a
novel method for synthesizing annotation data specifically designed for table
recognition. This method utilizes the structure and content of existing complex
tables, facilitating the efficient creation of tables that closely replicate
the authentic styles found in the target domain. By leveraging the actual
structure and content of tables from Chinese financial announcements, we have
developed the first extensive table annotation dataset in this domain. We used
this dataset to train several recent deep learning-based end-to-end table
recognition models. Additionally, we have established the inaugural benchmark
for real-world complex tables in the Chinese financial announcement domain,
using it to assess the performance of models trained on our synthetic data,
thereby effectively validating our method's practicality and effectiveness.
Furthermore, we applied our synthesis method to augment the FinTabNet dataset,
extracted from English financial announcements, by increasing the proportion of
tables with multiple spanning cells to introduce greater complexity. Our
experiments show that models trained on this augmented dataset achieve
comprehensive improvements in performance, especially in the recognition of
tables with multiple spanning cells.