Automated Evaluation of OCR Zoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Propagating Covariance in Computer Vision
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
Ground-truthing and benchmarking document page segmentation
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Data structures and tools for document database generation: an experimental system
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Evaluating SEE - A Benchmarking System for Document Page Segmentation
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
On benchmarking of invoice analysis systems
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
| Hi-index | 0.00 |
In order to increase the performance of document analysis systems a detailed quality evaluation of the achieved results is required. By focussing on segmentation algorithms, we point out that the results produced by the module under consideration should be evaluated directly; we will show that the text-based evaluation method which is often used in the document analysis domain does not accomplish the purpose of a detailed quality evaluation. Therefore, we propose a general evaluation approach for the comparison of segmentation results which is based on the segments directly. This approach is able to handle both algorithms that produce complete segmentations (partition) and algorithms that only extract objects of interest (extraction). Classes of errors are defined in a systematic way, and frequencies for each class can be computed. The evaluation approach is applicable to segmentation or extraction algorithms in a wide range. We have chosen the character segmentation task as an example in order to demonstrate the applicability of our evaluation approach, and we suggest to apply our approach to other segmentation tasks.