Abstract: Recognition of characters greatly depends upon the features used. Several features of the handwritten Arabic characters are selected and discussed. An off-line recognition system based on the selected features was built. The system was trained and tested with realistic samples of handwritten Arabic characters. Evaluation of the importance and accuracy of the selected features is made. The recognition based on the selected features give average accuracies of 88% and 70% for the numbers and letters, respectively. Further improvements are achieved by using feature weights based on insights gained from the accuracies of individual features.
Abstract: Current OCR technology does not allow to
accurately recognizing small text images, such as those found
in web images. Our goal is to investigate new approaches to
recognize very low resolution text images containing antialiased
character shapes.
This paper presents a preliminary study on the variability of
such characters and the feasibility to discriminate them by
using geometrical features. In a first stage we analyze the
distribution of these features. In a second stage we present a
study on the discriminative power for recognizing isolated
characters, using various rendering methods and font
properties. Finally we present interesting results of our
evaluation tests leading to our conclusion and future focus.
Abstract: Dealing with hundreds of features in character
recognition systems is not unusual. This large number of features
leads to the increase of computational workload of recognition
process. There have been many methods which try to remove
unnecessary or redundant features and reduce feature dimensionality.
Besides because of the characteristics of Farsi scripts, it-s not
possible to apply other languages algorithms to Farsi directly. In this
paper some methods for feature subset selection using genetic
algorithms are applied on a Farsi optical character recognition (OCR)
system. Experimental results show that application of genetic
algorithms (GA) to feature subset selection in a Farsi OCR results in
lower computational complexity and enhanced recognition rate.
Abstract: Optical Character Recognition (OCR) is a very old and of great interest in pattern recognition field. In this paper we introduce a very powerful approach to recognize Persian text. We have used morphological operators, especially Hit/Miss operator to descript each sub-word and by using a template matching approach we have tried to classify generated description. We used just one font in two different sizes to verify our approach. We achieved a very good rate, up to 99.9%.