ParameterFree Table Detection Method In this paper, we propose two parameterfree table detection methods: one for the closed tables and other for open tables. The unifying idea is multigaussian analysis. Multigaussian analysis of text height histograms classifies the document content into text and nontext blocks. Closed tables are classified as nontext and their identification from the nontext blocks is similar to many earlier methods that remove the separators. We do not need any parameters to identify rows and columns and discriminate them from text blocks because of multigaussian analysis. Open tables are initially classified as text blocks and are detected by extending the multigaussian analysis to the heights and widths of text blocks. The textblocks are grouped into three categories by multigaussian analysis. These groups are used to classify table cells and distinguish them from text blocks. Table blocks are merged to obtain the table region. Evaluation on various Indic script newspapers and ICDAR2013 table competition dataset shows that our methods achieve more than 90% in table recognition. The strength of our algorithm is that it is a parameterfree approach and requires no training dataset.

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.