The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of temporal information from static images of handwriting
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
An approach to integration of off-line and on-line recognition of handwriting
Pattern Recognition Letters
Large Vocabulary Recognition of On-Line Handwritten Cursive Words
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Pen-Input Character Recognition System Based on Integration of Online-Offline Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Off-Line Cursive Handwriting Recognition Compared with On-Line Recognition
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Direction-Change Features of Imaginary Strokes for On-Line Handwriting Character Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
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In this paper, we present a novel method to extract stroke order independent information from online data. This information, which we term pseudo-online, conveys relevant information on the offline representation of the word. Based on this information, a combination of classification decisions from online and pseudo-online cursive word recognizers is performed to improve the recognition of online cursive words. One of the most valuable aspects of this approach with respect to similar methods that combine online and offline classifiers for word recognition is that the pseudo-online representation is similar to the online signal and, hence, word recognition is based on a single engine. Results demonstrate that the pseudo-online representation is useful as the combination of classifiers perform better than those based solely on pure online information.