Structured files, like spreadsheets, are valuable sources of data, but often ill-suited for machine-consumption. Although spreadsheets contain cells in a grid-like structure, the data they contain is often arranged with a free layout, with no clearly defined tabular structure. Or worse, tables are arranged in several, independent regions that have to ultimately be recognized and merged by end-users which are interested in their content. In light of automated data preparation, extraction, or integration, there is great value in recognizing the presence and layout of regions, especially tables, within a spreadsheet.

Table recognition is a well-known problem, tackled by different researchers on various domains, and with different assumptions. In this seminar, we will introduce you to the research area of table recognition in spreadsheet files. Each team, ideally consisting of 2 students, will explore, implement and potentially improve on a different solution to detect and extract tables from spreadsheet files.

We will provide you with state of the art papers that suggest solutions to the above problem, which you will implement and try to improve upon with your own ideas in a scalable way. We will provide thousands of files for testing and evaluation.

When: Wednesdays 15:15 at Campus II, Building F, Room F-2-11 / online on Zoom. The following timetable is still tentative.

You can find the following papers on dblp or google-scholar:

• Barik, T., Lubick, K., Smith, J., Slankas, J. and Murphy-Hill, E. 2015. Fuse: A Reproducible, Extendable, Internet-Scale Corpus of Spreadsheets. IEEE/ACM Working Conference on Mining Software Repositories.

• Christodoulakis, C., Munson, E.B. and Gabel, M. 2020. Pytheas: Pattern-based Table Discovery in CSV Files. PVLDB

• Dong, H., Liu, S., Han, S., Fu, Z. and Zhang, D. 2019. TableSense: Spreadsheet Table Detection with Convolutional Neural Networks.Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

• Gilani, A., Qasim, S.R., Malik, I. and Shafait, F. 2017. Table Detection Using Deep Learning.IAPR International Conference on Document Analysis and Recognition (ICDAR)

• Hermans, F. and Murphy-Hill, E. 2015. Enron’s Spreadsheets and Related Emails: A Dataset and Analysis. IEEE/ACM IEEE International Conference on Software Engineering (ICSE)

• Fisher, M. and Rothermel, G. 2005. The EUSES spreadsheet corpus: a shared resource for supporting experimentation with spreadsheet dependability mechanisms. ACM SIGSOFT Software Engineering Notes.

• Koci, E., Thiele, M., Romero, O. and Lehner, W. 2017. Table Identification and Reconstruction in Spreadsheets. International Conference on Document Analysis and Recognition (ICDAR)

• Koci, E., Thiele, M., Rehak, J., Romero, O. and Lehner, W. 2019. DECO: A Dataset of Annotated Spreadsheets for Layout and Table Recognition. International Conference on Advanced Information Systems Engineering (CAISE)

• Koci, E., Thiele, M., Romero, O. and Lehner, W. 2019. A Genetic-Based Search for Adaptive Table Recognition in Spreadsheets. International Conference on Document Analysis and Recognition (ICDAR

• Mitlohner, J., Neumaier, S., Umbrich, J. and Polleres, A. 2016. Characteristics of Open Data CSV Files. International Conference on Open and Big Data (OBD)

By Topic:

Datasets:

• Hermans, F.  and Murphy-Hill,E. 2015. Enron’s Spreadsheets and Related Emails: A Dataset and Analysis. IEEE International Conference on Software Engineering (ICSE).

• Barik, T., Lubick, K., Smith, J., Slankas, J. and Murphy-Hill, E. 2015. Fuse: A Reproducible, Extendable, Internet-Scale Corpus of Spreadsheets. IEEE/ACM Working Conference on Mining Software Repositories.

• Fisher, M. and Rothermel, G. 2005. The EUSES spreadsheet corpus: a shared resource for supporting experimentation with spreadsheet dependability mechanisms. ACM SIGSOFT Software Engineering Notes.

• Koci, E., Thiele, M., Rehak, J., Romero, O. and Lehner, W. 2019. DECO: A Dataset of Annotated Spreadsheets for Layout and Table Recognition. International Conference on Document Analysis and Recognition (ICDAR)

• Mitlohner, J., Neumaier, S., Umbrich, J. and Polleres, A. 2016. Characteristics of Open Data CSV Files. International Conference on Open and Big Data (OBD)

• Zanibbi, R., Blostein, D. and Cordy, James R. 2004. A survey of table recognition: Models, observations, transformations, and inferences. Document Analysis and Recognition.

Table Recognition

• Christodoulakis, C., Munson, E.B. and Gabel, M. 2020. Pytheas: Pattern-based Table Discovery in CSV Files. PVLDB

• Dong, H., Liu, S., Han, S., Fu, Z. and Zhang, D. 2019. TableSense: Spreadsheet Table Detection with Convolutional Neural Networks.Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

• Gilani, A., Qasim, S.R., Malik, I. and Shafait, F. 2017. Table Detection Using Deep Learning.IAPR International Conference on Document Analysis and Recognition (ICDAR)

• Koci, E., Thiele, M., Rehak, J., Romero, O. and Lehner, W. 2019. DECO: A Dataset of Annotated Spreadsheets for Layout and Table Recognition. International Conference on Advanced Information Systems Engineering (CAISE)

• Koci, E., Thiele, M., Romero, O. and Lehner, W. 2019. A Genetic-Based Search for Adaptive Table Recognition in Spreadsheets. International Conference on Document Analysis and Recognition (ICDAR

Here we list the datasets and their annotations useful for table recognition. Note that due to license issues, only publicly distributable datasets are listed here. Each link points to a compressed json file that includes both the verbose CSV files and their annotations.

• Project seminar for master students 
• Language of instruction: English
• Maximum number of participants: 6

Students form teams of one or two members. Each team is assigned a task and the according publications. After studying this (and further) literature, the teams should present a summary of the state of the art solution and in parallel also have a practical implementation. To present the baseline and the results of the first phase to the whole group, all teams will give a mid-term presentations.

In the second half of the seminar, each team tries to improve or find a better solution for thier task. The team members should finally report on their improvements in a last presentation. To conclude the seminar, each team needs to prepare a paper-style submission of thier solution .