Graphs and algorithms
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Recovery of temporal information from static images of handwriting
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Drawing Order from Single-Stroke Handwriting Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
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
Multiple Classifier Combination Methodologies for Different Output Levels
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
The IRESTE On/Off (IRONOFF) Dual Handwriting Database
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
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
Recovery of Writing Sequence of Static Images of Handwriting using UWM
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
N-Gram and N-Class Models for On line Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Off-line handwritten word recognition using multi-stream hidden Markov models
Pattern Recognition Letters
Binary segmentation with neural validation for cursive handwriting recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Techniques for static handwriting trajectory recovery: a survey
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Binary segmentation algorithm for English cursive handwriting recognition
Pattern Recognition
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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This paper analyses a handwriting recognition system for offline cursive words based on HMMs. It compares two approaches for transforming offline handwriting available as two-dimensional images into one-dimensional input signals that can be processed by HMMs. In the first approach, a left-right scan of the word is performed resulting in a sequence of feature vectors. In the second approach, a more subtle process attempts to recover the temporal order of the strokes that form words as they were written. This is accomplished by a graph model that generates a set of paths, each path being a possible temporal order of the handwriting. The recognition process then selects the most likely temporal stroke order based on knowledge that has been acquired from a large set of handwriting samples for which the temporal information was available. We show experimentally that such an offline recognition system using the recovered temporal order can achieve recognition performances that are much better than those obtained with the simple left-right order, and that come close to those of an online recognition system. We have been able to assess the ordering quality of handwriting when comparing true ordering and recovered one, and we also analyze the situations where offline and online information differ and what the consequences are on the recognition performances. For these evaluations, we have used about 30,000 words from the IRONOFF database that features both the online signal and offline signal for each word.