Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel and quick SVM-based multi-class classifier
Pattern Recognition
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text
International Journal on Document Analysis and Recognition
Automatic Writer Identification of Ancient Greek Inscriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text line and word segmentation of handwritten documents
Pattern Recognition
HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Signature Detection and Matching for Document Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Conscience On-line Learning Approach for Kernel-Based Clustering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Energy based competitive learning
Neurocomputing
Conscience online learning: an efficient approach for robust kernel-based clustering
Knowledge and Information Systems
-NS: A Classifier by the Distance to the Nearest Subspace
IEEE Transactions on Neural Networks
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In handwritten character recognition, it is a significant step to segment a text line into characters. The unsupervised clustering is a common approach for this task. However, due to the strong overlapping and touch among characters, the separation boundaries between two characters are usually nonlinear, which leads to the failure of the widely used clustering methods such as k-means. To tackle this problem, this paper proposes a new handwritten character segmentation method based on nonlinear clustering methods. In the proposed approach, we first segment the entire text line into strokes, the similarity matrix of which is computed according to stroke gravities. Then, the nonlinear clustering methods are performed on this similarity matrix to obtain cluster labels for these strokes. According to the obtained cluster labels, the strokes are combined to form characters. In this paper, we consider two nonlinear clustering methods, namely, spectral clustering based on Normalized cut (Ncut) and kernel clustering based on Conscience On-Line Learning (COLL). Whereby, two segmentation approaches are proposed with the one using Ncut termed SegNcut, and the one using COLL termed SegCOLL. Experiments on four databases are conducted to demonstrate the effectiveness of our SegNcut and SegCOLL approaches.