Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
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A Maximum-Likelihood Approach to Segmentation-Based Recognition of Unconstrained Handwriting Text
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Handwritten Numeral String Recognition: Character-Level vs. String-Level Classifier Training
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Learning to Group Text Lines and Regions in Freeform Handwritten Notes
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Online Handwritten Japanese Character String Recognition Incorporating Geometric Context
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
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A robust approach to text line grouping in online handwritten Japanese documents
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Online Handwritten Japanese Character String Recognition Using Conditional Random Fields
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ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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ICDAR 2011 Chinese Handwriting Recognition Competition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Improving Handwritten Chinese Text Recognition by Confidence Transformation
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
CASIA Online and Offline Chinese Handwriting Databases
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Context driven chinese string segmentation and recognition
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Recognition of online handwritten mathematical expressions
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Handwritten Chinese Text Recognition by Integrating Multiple Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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With the advances of handwriting capturing devices and computing power of mobile computers, pen-based Chinese text input is moving from character-based input to sentence-based input. This paper proposes a real-time recognition approach for sentence-based input of Chinese handwriting. The main feature of the approach is a dynamically maintained segmentation-recognition candidate lattice that integrates multiple contexts including character classification, linguistic context and geometric context. Whenever a new stroke is produced, dynamic text line segmentation and character over-segmentation are performed to locate the position of the stroke in text lines and update the primitive segment sequence of the page. Candidate characters are then generated and recognized to assign candidate classes, and linguistic context and geometric context involving the newly generated candidate characters are computed. The candidate lattice is updated while the writing process continues. When the pen lift time exceeds a threshold, the system searches the candidate lattice for the result of sentence recognition. Since the computation of multiple contexts consumes the majority of computing and is performed during writing process, the recognition result is obtained immediately after the writing of a sentence is finished. Experiments on a large database CASIA-OLHWDB of unconstrained online Chinese handwriting demonstrate the robustness and effectiveness of the proposed approach.