IEEE Transactions on Pattern Analysis and Machine Intelligence
Baseline Estimation For Arabic Handwritten Words
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Substroke Approach to HMM-Based On-line Kanji Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Recognising handwritten Arabic manuscripts using a single hidden Markov model
Pattern Recognition Letters
HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT- Database
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
On-line Overlaid-Handwriting Recognition Based on Substroke HMMs
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Short communication: Variable space hidden Markov model for topic detection and analysis
Knowledge-Based Systems
Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream
Neural Processing Letters
Online Character Recognition Using Elastic Curvature Matching
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
On-line Arabic handwriting recognition with templates
Pattern Recognition
Hierarchical On-line Arabic Handwriting Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
An efficient hybrid approach for online recognition of handwritten symbols
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
IEEE Transactions on Image Processing
A new automatic identification system of insect images at the order level
Knowledge-Based Systems
The optical character recognition of Urdu-like cursive scripts
Pattern Recognition
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Urdu script-based languages' character recognition has some technical issues not existing in other languages and makes these languages more complicated. Segmentation-based character recognition approach for handwritten Urdu, both Nasta'liq and Nasakh script-based languages, incorporates number of overhead and very less accurate as compared to segmentation free. This paper presents a segmentation-free approach for recognition of online Urdu handwritten script using hybrid classifier, HMM and fuzzy logic. Trained data set consisting of HMMs for each stroke is further classified into 62 sub-patterns based on the primary stroke shape at the beginning and end using fuzzy rule. Fuzzy linguistic variables based on language structure are used to model features and provide suitable result for large variation in handwritten strokes. Twenty-six time variant structural and statistical features are extracted for the base strokes. The fuzzy classification into sub-patterns increases the efficiency and decreases the computational complexity due to reduction in data set size. The hybrid HMM-fuzzy technique is efficient for large and complex data set. It provided 87.6% and 74.1% for Nasta'liq and Nasakh, respectively, on 1800 ligatures.