This paper addresses the automatic recognition of handwritten temperature values in weather records. The localization of table cells is based on line detection using projection profiles. Further, a stroke-preserving line removal method which is based on gradient images is proposed. The presented digit recognition utilizes features which are extracted using a set of filters and a Support Vector Machine classifier. It was evaluated on the MNIST and the USPS dataset and our own database with about 17,000 RGB digit images. An accuracy of 99.36% per digit is achieved for the entire system using a set of 84 weather records.
@article{arxiv.1304.6933,
title = {Digit Recognition in Handwritten Weather Records},
author = {Manuel Keglevic and Robert Sablatnig},
journal= {arXiv preprint arXiv:1304.6933},
year = {2013}
}
Comments
Part of the OAGM/AAPR 2013 proceedings (arXiv:1304.1876), 8 pages