Floating search methods in feature selection
Pattern Recognition Letters
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Digital Image Processing
Writer Identification Using Text Line Based Features
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ImprovingWriter Identification by Means of Feature Selection and Extraction
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A writer identification and verification system
Pattern Recognition Letters
Pattern Recognition Letters
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A writer identification system for on-line whiteboard data
Pattern Recognition
Arabic writer identification based on hybrid spectral-statistical measures
Journal of Experimental & Theoretical Artificial Intelligence
Fractal and Multi-fractal for Arabic Offline Writer Identification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An Efficient Method for Offline Text Independent Writer Identification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Handwriting is one of the most famous biometrics which is processed based on image processing and pattern recognition techniques. However, there are a lot of reports that have already been published on handwritten text identification methods and researchers try to improve the accuracy and speed of such methods. This paper presents an offline Persian handwriting identification method in which some new text features are extracted and best ones are selected using a swarm-based approach. The essence of this feature selection method is bees algorithm, which is a modern swarm-based meta-heuristic approach. In the proposed technique, the adaptive neuro-fuzzy inference system ANFIS is employed as classifier and trained by the input feature vectors. It is also compared with a multi-layer perceptron MLP and fuzzy K-nearest neighbour classifiers. To test the proposed method, we have collected a handwritten Persian text dataset from 125 people who have written six sheets with five lines in each of optional Persian texts. Experimental results showed that the prediction accuracy was about 98% in average while the method training time is less than most related works. It seems this method can be extended for other languages by adjusting its parameters.