  Handwriting recognition is one of the active and challenging areas of
research in the field of image processing and pattern recognition. It has many
applications that include: a reading aid for visual impairment, automated
reading and processing for bank checks, making any handwritten document
searchable, and converting them into structural text form, etc. Moreover, high
accuracy rates have been recorded by handwriting recognition systems for
English, Chinese Arabic, Persian, and many other languages. Yet there is no
such system available for offline Kurdish handwriting recognition. In this
paper, an attempt is made to design and develop a model that can recognize
handwritten characters for Kurdish alphabets using deep learning techniques.
Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian
based script with modified alphabets. In this work, a Deep Convolutional Neural
Network model is employed that has shown exemplary performance in handwriting
recognition systems. Then, a comprehensive dataset was created for handwritten
Kurdish characters, which contains more than 40 thousand images. The created
dataset has been used for training the Deep Convolutional Neural Network model
for classification and recognition tasks. In the proposed system, the
experimental results show an acceptable recognition level. The testing results
reported a 96% accuracy rate, and training accuracy reported a 97% accuracy
rate. From the experimental results, it is clear that the proposed deep
learning model is performing well and is comparable to the similar model of
other languages' handwriting recognition systems.
