Recovery of temporal information from static images of handwriting
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Holistic handwritten word recognition using temporal features derived from off-line images
Pattern Recognition Letters
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of temporal information of cursively handwritten words for on-line recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Strokes recovering from static handwriting
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
IEEE Transactions on Image Processing
Verification of dynamic curves extracted from static handwritten scripts
Pattern Recognition
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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.