TY  - EJOU
AU  - Ahmed, Gulzar 
AU  - Alyas, Tahir 
AU  - Iqbal, Muhammad Waseem 
AU  - Ashraf, Muhammad Usman 
AU  - Alghamdi, Ahmed Mohammed 
AU  - Bahaddad, Adel A. 
AU  - Almarhabi, Khalid Ali 

TI  - Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 73
IS  - 2
SN  - 1546-2226

AB  - Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches.
KW  - Urdu handwritten text recognition; handwritten dataset; convolutional neural network; artificial intelligence; machine learning; deep learning

DO  - 10.32604/cmc.2022.029314