S. report a recognition rate of 87.6% and
S. Malik and S.A. Khan used “a rule based slant
analysis and conversion” for online Urdu handwriting recognition. Their system
is able to recognize isolated Urdu characters, numbers, and 200 two character
Urdu words with a recognition rate of 93% for isolated characters and numbers
and 78% for two character words. S.A. Hussain et al. used a segmentation free
approach with 20 di?erent
structural features for recognition of 850 single character, 2 character and 3
characters ligatures enabling recognition of 18000 common words of Urdu dictionary.
They used BPNN (back Propagation Neural Network) as a classifier with accuracy
of 93% for base ligatures and 98% for secondary ligatures. M.I. Razzak and S.A.
Hussain presented a segmentation free approach for recognition of online Urdu
text using a hybrid classifier of HMM and Fuzzy Logic. Authors report a
recognition rate of 87.6% and 74.1% for Nastaliq and Naskh styles respectively.
K.U. Khan and I. Haider applied various classifier such as correlation based
classifier, back propagation neural network classifier and probabilistic neural
network based classifier on isolated online handwritten Urdu characters and
found that probabilistic neural network based classifier works best. A database
of 110 instances of handwritten Urdu characters from 40 individuals of di?erent age groups was used and recognition rate
of 94% to 98% was reported for 4 di?erent groups of Urdu characters set classified on the bases of number of
strokes. M. I. Razzak et al. applied combined online and o?ine preprocessing techniques on Urdu text for
improving e?ciency of the Urdu character recognition process.
Z. Ahmed and J. K. Orakzai used feed forward neural network for recognition of
offline Urdu text. Size of the input text was kept constant and text was
assumed to be diacritic free. They report a recognition rate of 93.4%. T. Nawaz
et al. Appied pattern matching technique
on the chain code for the recognition of isolated Urdu characters in Naskh
style. They report a recognition rate of 89%. I. Shamsher et al. also used feed
forward neural network for recognition of isolated Urdu characters. They report
accuracy of 98.3%. S. A. Hussain et al.used Kohonen Self organizing Map (KSOM)
for pre segmented Urdu characters in Naskh style. Their system can handle 104
segmented character ligatures with 80% accuracy. S. Sardar and A. Wahab used K Nearest
Neighbor (KNN) algorithm for isolated online and offline Urdu characters using
5 features. They report a recognition rate of 97.12%.