Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reading cursive handwriting by alignment of letter prototypes
International Journal of Computer Vision
Topology representing networks
Neural Networks
Machine Learning
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
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An Off-Line Cursive Handwriting Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
The handbook of brain theory and neural networks
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cursive character recognition by learning vector quantization
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Generalized Projections: A Tool for Cursive Handwriting Normalization
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Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A novel approach for structural feature extraction: contour vs. direction
Pattern Recognition Letters
Experimental Analysis of the Modified Direction Feature for Cursive Character Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Neural Networks
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Expert Systems with Applications: An International Journal
A neuro-fuzzy inference engine for Farsi numeral characters recognition
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Dynamic pattern denoising method using multi-basin system with kernels
Pattern Recognition
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
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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Multi-scale image segmentation algorithm based on support vector machine approximation criteria
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ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A MDRNN-SVM hybrid model for cursive offline handwriting recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words
ACM Transactions on Asian Language Information Processing (TALIP)
Feature representation selection based on Classifier Projection Space and Oracle analysis
Expert Systems with Applications: An International Journal
Effect of ensemble classifier composition on offline cursive character recognition
Information Processing and Management: an International Journal
Recognition of offline handwritten numerals using an ensemble of MLPs combined by Adaboost
Proceedings of the 4th International Workshop on Multilingual OCR
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This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines (SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition.