Digit Recognition in Handwritten Weather Records
This work addresses a domain-specific problem for digit recognition in historical or handwritten weather data, but it is incremental as it applies existing methods like SVM and line detection to a new dataset.
The paper tackles the problem of automatically recognizing handwritten temperature values in weather records by localizing table cells and using a stroke-preserving line removal method, achieving an accuracy of 99.36% per digit on a dataset of 84 weather records.
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.