TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words
Arushi Jain, Shubham Singh Paliwal, Monika Sharma, Lovekesh Vig · ESANN 2021 proceedings · 2021
Existing methods for Table Structure Recognition (TSR) from camera-captured or scanned documents perform poorly on complextables consisting of nested rows / columns, multi-line texts and missing cell data.This is because current data-driven methods work by simply training deep models on large volumes of data and fail to generalize when an unseen table structure is encountered.In this paper, we propose to train a deep network to capture the spatial associations between different word pairs present in the table image for unravelling the table structure.We present an end-to-end pipeline, named TSR-DSAW: TSR via Deep Spatial Association of Words, which outputs a digital representation of a table image in a structured format such as HTML.Given a table image as input, the proposed method begins with the detection of all the words present in the image using a text-detection network like CRAFT which is followed by the generation of word-pairs using dynamic programming.These word-pairs are highlighted in individual images and subsequently, fed into a DenseNet-121 classifier trained to capture spatial associations such as same-row, same-column, same-cell or none.Finally, we perform post-processing on the classifier output to generate the table structure in HTML format.We evaluate our TSR-DSAW pipeline on two public table-image datasets -PubTabNet and ICDAR 2013, and demonstrate improvement over previous methods such as TableNet and DeepDeSRT.