DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Combining Online and Offline Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integration of Online and Pseudo-Online Information for Cursive Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition-directed recovering of temporal information from handwriting images
Pattern Recognition Letters
Intelligent understanding of handwritten geometry theorem proving
Proceedings of the 15th international conference on Intelligent user interfaces
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Recent results of online Japanese handwriting recognition and its applications
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
A neural network model for online handwritten mathematical symbol recognition
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Pattern Recognition Letters
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We will describe a handwriting character recognition system that integrates offline recognition requiring a bitmap image and online recognition involving an input pattern as a sequence of x-y coordinates.Offline recognition performs well for painted or overwritten patterns for which online recognition would not be suited, whereas online recognition is suitable for very deformed patterns for which offline recognition is not suited. Because each method has different recognition capabilities, the methods complement each other when integrated them. We have implemented a hybrid handwriting character recognition system in which the recognition results of the offline and online recognizer are integrated to create an improved product. After testing several integration methods for a handwriting character database, we found that the best method increased the recognition rate from 73.8% (offline) and 84.8% (online) to 87.6% (integrated).