Directed connection measurement for evaluating reconstructed stroke sequence in handwriting images
Generally speaking, the performance of on-line handwriting recognition systems is better than that of off-line handwriting recognition systems. Many researchers then started to extract/estimate on-line/temporal information from a static handwritten image to improve the performance of off-line recognition systems. More specifically, various algorithms have been developed to extract the stroke sequence, which is an important temporal information, from a static handwritten image in the last decade. However, to the best of our knowledge, there are no methods to evaluate the performance of these algorithms. In view of this limitation, this paper presents a directed connection measurement for evaluating the performance of these methods. The measurement is designed in terms of direction and connection. The direction measurement is based on the consecutive arrangements of the items, while the connection measurement is based on both the number of disconnections and the Feigin and Cohen model (FCM) in ranking analysis. We replace the Kendall's distance, the key metric component in FCM, by a new connection metric. We have also proved that the new direction and connection metrics satisfy all axioms for being a valid metric. The two correlations are combined to form a directed connection correlation, which is applicable in comparing the performance of different stroke sequence recovery algorithms.
handwriting recognition ranking analysis stroke sequence estimation