A new segmentation technique for omnifont Farsi text
Pattern Recognition Letters
Pattern Recognition Letters
A novel feature extraction method and hybrid tree classification for handwritten numeral recognition
Pattern Recognition Letters
Recognition of Handwritten Dates on Bank Checks using an HMM Approach
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Recognising handwritten Arabic manuscripts using a single hidden Markov model
Pattern Recognition Letters
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
A Novel Fuzzy Approach to Recognition of Online Persian Handwriting
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Fast Zernike wavelet moments for Farsi character recognition
Image and Vision Computing
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
A SVM-based cursive character recognizer
Pattern Recognition
Lexicon reduction using dots for off-line Farsi/Arabic handwritten word recognition
Pattern Recognition Letters
Mathematical symbol recognition with support vector machines
Pattern Recognition Letters
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
A hybrid method for robust car plate character recognition
Engineering Applications of Artificial Intelligence
A multiple classifier approach to detect Chinese character recognition errors
Pattern Recognition
Display text segmentation after learning best-fitted OCR binarization parameters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
Hi-index | 12.05 |
Character recognition of Farsi and Arabic texts as an open and demanding problem needs to encounter sophisticated specifications of the characters such as their shapes, continuity, dots and also, different fonts. Utilizing fuzzy set theory as a tolerant approach toward uncertainty and vagueness and artificial neural networks as a machine learning method in this paper, we propose a neuro-fuzzy inference engine to recognize the Farsi numeral characters. This engine takes holistic approach of character recognition through the comparison of the unknown character's features with the features of the existing characters that itself is characterized through Mamdani inference engine on fuzzy rules which is largely enhanced with a multi layer perceptron neural network's learning on features of the different fonts' characters which leads to more comprehensive recognition of Farsi numeral characters in the proposed system. Having applied this novel engine on a dataset of unknown numeral characters consisted of 33 different Farsi fonts, it yielded more accurate results than the corresponding researches. The recognition rates of unknown numeral characters are greater than 97% except for Farsi character 4, so as the proposed schema could not score a better result than 95% for this numeral character which implies its recognition is still in need of more enhancement.