Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Segmentation Performance on the CEDAR Benchmark Database
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Contour Code Feature Based Segmentation For Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Automatic Segmentation and Recognition System for Handwritten Dates on Canadian Bank Cheques
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A novel approach for structural feature extraction: contour vs. direction
Pattern Recognition Letters
The Neural-based Segmentation of Cursive Words using Enhanced Heuristics
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Recognition-directed recovering of temporal information from handwriting images
Pattern Recognition Letters
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of information retrieval techniques to single writer documents
Pattern Recognition Letters
Maximization of Mutual Information for Offline Thai Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rejection strategies for offline handwritten text line recognition
Pattern Recognition Letters
Holistic cursive word recognition based on perceptual features
Pattern Recognition Letters
A SVM-based cursive character recognizer
Pattern Recognition
On-line handwritten digit recognition based on trajectory and velocity modeling
Pattern Recognition Letters
Lexicon reduction using dots for off-line Farsi/Arabic handwritten word recognition
Pattern Recognition Letters
Persian/arabic handwritten word recognition using M-band packet wavelet transform
Image and Vision Computing
Pattern Recognition Letters
Filtering segmentation cuts for digit string recognition
Pattern Recognition
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
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Segmentation in off-line cursive handwriting recognition is a process for extracting individual characters from handwritten words. It is one of the most difficult processes in handwriting recognition because characters are very often connected, slanted and overlapped. Handwritten characters differ in size and shape as well. Hybrid segmentation techniques, especially over-segmentation and validation, are a mainstream to solve the segmentation problem in cursive off-line handwriting recognition. However, the core weakness of the segmentation techniques in the literature is that they impose high risks of chain failure during an ordered validation process. This paper presents a novel Binary Segmentation Algorithm (BSA) that reduces the risks of the chain failure problems during validation and improves the segmentation accuracy. The binary segmentation algorithm is a hybrid segmentation technique and it consists of over-segmentation and validation modules. The main difference between BSA and other techniques in the literature is that BSA adopts an un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are very promising.