Real-time Arabic handwritten character recognition
Pattern Recognition
Optimal combinations of pattern classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experimental study of a novel neuro-fuzzy system for on-line handwritten UNPEN digit recognition
Pattern Recognition Letters
A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Piecewise Linear Modulation Model of Handwriting
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An Evolutionary Neuro-Fuzzy Approach to Recognize On-Line Arabic Handwriting
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Comparison of Techniques for Automatic Clustering of Handwritten Characters
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
The Delta LogNormal theory for the generation and modeling of cursive characters
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
On-Line Recognition of Handwritten Arabic Characters Using a Kohonen Neural Network
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Handwriting Trajectory Movements Controlled by a Bêta-Elliptic Model
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Analysis of errors of handwritten digits made by a multitude of classifiers
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Hybrid generative/discriminative classifier for unconstrained character recognition
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Printed PAW Recognition Based on Planar Hidden Markov Models
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
On adaptive confidences for critic-driven classifier combining
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Hybrid fuzzy-neural systems in handwritten word recognition
IEEE Transactions on Fuzzy Systems
Potential improvement of classifier accuracy by using fuzzy measures
IEEE Transactions on Fuzzy Systems
A fuzzy logic system for the detection and recognition of handwritten street numbers
IEEE Transactions on Fuzzy Systems
Comparison of crisp and fuzzy character neural networks in handwritten word recognition
IEEE Transactions on Fuzzy Systems
An Approach to Searching for Two-Dimensional Cellular Automata for Recognition of Handwritten Digits
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Off-line handwriting recognition system based on GA and visual encoding
Proceedings of the International Workshop on Multilingual OCR
Binary segmentation with neural validation for cursive handwriting recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An Iterative Method for Deciding SVM and Single Layer Neural Network Structures
Neural Processing Letters
Precise and accurate decimal number recognition using Global Motion Estimation
International Journal of Artificial Intelligence and Soft Computing
Binary segmentation algorithm for English cursive handwriting recognition
Pattern Recognition
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Global feature for online character recognition
Pattern Recognition Letters
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The handwriting is one of the most familiar communication media. Pen based interface combined with automatic handwriting recognition offers a very easy and natural input method. The handwritten signal is on-line collected via a digitizing device, and it is classified as one pre-specified set of characters. The main techniques applied in our work include two fields of research. The first one consists of the modeling system of handwriting. In this area, we developed a novel method of the handwritten trajectory modeling based on elliptic and Beta representation. The second part of our work shows the implementation of a classifier consisting of the Multi-Layers Perception of Neural Networks (MLPNN) developed in a fuzzy concept. The training process of the recognition system is based on an association of the Self Organization Maps (SOM) with Fuzzy K-Nearest Neighbor Algorithms (FKNNA). To test the performance of our system we build 30,000 Arabic digits. The global recognition rate obtained by our recognition system is about 95.08%.