An Approach towards Benchmarking of Table Structure Recognition Results

After developing a model free table recognition system we wanted to tune parameters in order to optimize the recognition performance. Therefore we developed a benchmarking environment, including a user frontend to acquire ground truth and mechanisms to evaluate the quality of the recognition results. The tasks involved in the analysis systems were the locating of table regions, identification of cells and mapping of cells to rows and columns. This paper presents our approach towards the comparison of recognition results with the ground truth. The established definitions of recall and precision did not meet our requirements, as we wanted to register even smallest improvements (or changes in general) in the results, even when both results were imperfect. We therefore extended the measures recall and precision in order to deal with recognition probabilities of objects rather than just with boolean values.

• Type: Conference
• Year: 2005
• Where: ICDAR
• Authors: Thomas Kieninger, Andreas Dengel
• Keywords: Document Analysis, Free Table Recognition, Ground Truth, ICDAR 2005, Recognition Performance