Rethinking Semantic Segmentation for Table Structure Recognition in Documents Based on the recent advancements in the domain of semantic segmentation, FullyConvolutional Networks (FCN) have been successfully applied for the task of table structure recognition in the past. We analyze the efficacy of semantic segmentation networks for this purpose and simplify the problem by proposing prediction tiling based on the consistency assumption which holds for tabular structures. For an image of dimensions H xc3x97 W, we predict a single column for the rows (xc5xb7_row xe2x88x8a H) and a predict a single row for the columns (xc5xb7_row xe2x88x8a W). We use a dualheaded architecture where initial feature maps (from the encoderdecoder model) are shared while the last two layers generate class specific (rowcolumn) predictions. This allows us to generate predictions using a single model for both rows and columns simultaneously, where previous methods relied on two separate models for inference. With the proposed method, we were able to achieve stateoftheart results on ICDAR13 imagebased table structure recognition dataset with an average FMeasure of 92.39% (91.90% and 92.88% FMeasure for rows and columns respectively). With the proposed method, we were able to achieve stateoftheart results on ICDAR13. The obtained results advocate that constraining the problem space in the case of FCN by imposing valid constraints can lead to significant performance gains.

DeepTabStR: Deep Learning based Table Structure Recognition This paper presents a novel method for the analysis of tabular structures in document images using the potential of deformable convolutional networks. In order to assess the suitability of the model to the task of table structure recognition, most of the prior methods have been tested on the smaller ICDAR13 table structure recognition dataset comprising of just 156 tables. We curated a new imagebased table structure recognition dataset, TabStructDB2, comprising of 1081 tables densely labeled with row and column information. Instead of collecting new images for this purpose, we leveraged the famous PageObject Detection dataset from ICDAR17, and added structural information for all the tabular regions present in the dataset. This new publicly available dataset will enable the development of more sophisticated table structure recognition techniques in the future. We performed extensive evaluation on the two datasets (ICDAR13 and TabStructDB) including crossdataset testing in order to evaluate the efficacy of the proposed approach. We achieved stateoftheart results with deformable models on ICDAR13 with an average FMeasure of 92.98% (89.42% for rows and 96.55% for columns) and report baseline results on TabStructDB for guiding future research efforts with an FMeasure of 93.72% (91.26% for rows and 95.59% for columns). Despite promising results, structural analysis of tables with arbitrary layouts is still far from achievable at this point.