An End-to-End Multi-Task Learning Model for Image-based Table Recognition

Understand

Image-based table recognition is a challenging task due to the diversity of table styles and the complexity of table structures.

• Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each sub-problem independently using two separate systems.
• In this paper, we propose an end-to-end multi-task learning model for image-based table recognition.
• The proposed model consists of one shared encoder, one shared decoder, and three separate decoders which are used for learning three sub-tasks of table recognition: table structure recognition, cell detection, and cell-content recognition.