# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 1 Introduction  
In the field of pattern recognition and computer vision research, the task of handwritten text recognition is regarded as one of the most challenging areas. The cursive nature of text, the shape similarity of individual characters, and the availability of different writing styles are some of the key issues that make the recognition task more challenging. While recognizing the isolated word and character in the printed text, higher accuracy rates are observed in the literature; however, there is a need for an efficient recognition system that gives remarkable results in recognizing handwritten texts [1; 2; 3; 4; 5]. Urdu is one the cursive languages that is widely spoken and written in the regions of South-East Asia including Pakistan, India, Bangladesh, Afghanistan, etc. [6]. Optical character recognition (OCR) of Urdu script started in late 2000, and the first work on Urdu OCR was published in 2004. The literature review identified the fact that there is a lack of research efforts in Urdu handwritten text recognition as compared to the recognition of the scripts of other languages [4; 7; 8; 9]. Furthermore, there are a few Urdu OCR systems for printed text that are commercially available [10; 11], but there is no system available for recognition of Urdu handwritten text to date. It is pertinent to mention that in the field of computer vision and pattern recognition, handwritten text recognition is termed ICR (intelligent character recognition), while analysis of the printed text is known as OCR (optical character recognition). In the text, we use ICR for handwritten text recognition. It is observed that several machine learning models like SVM (support vector machines) [12], NB (naive Bayes) [13], ANN (artificial neural network) [14; 15], etc., were applied in the analysis of Urdu handwritten text in the literature survey. We also proved the competitiveness of the above approaches in analyzing the text images. In the literature, many researchers recommend using CNN (convolutional neural networks) [16; 17] in extracting information from the images having text data. Furthermore,the notable work reported in [18; 19; 20] concluded that CNN is one of the most commonly-used DNNs (deep neural networks) in image processing while performing complex tasks like pattern matching, pattern analysis, etc. Furthermore, CNN is equally applicable to the data corpus at either the word or character level, without any prior knowledge of the syntactic (or semantic) structures of the language. The CNN model is equally applicable in a variety of data science-related tasks ranging from computer vision applications to speech recognition and others. The reason behind the successive usage of DNN is that these network models build the required correct mathematical relationship between the given input and the output regardless of the underlying nature of the model whether it is linear or non-linear. Moreover, the information in the DNN moves through the underlying layers calculating the probability of each output. This capability makes DNN one of the reliable and efficient models for solving the tasks mentioned earlier. Furthermore, the capability of deep learning models of extracting and identifying the peculiar features plays a key role in generating incisive and reliable results to the researchers. These approaches have also been proven to be competitive with traditional models. The literature related to Urdu handwritten text recognition [21; 22; 23; 24; 25; 26; 27] also recommended deep network models to get more optimal results in the minimum time. To our knowledge, this paper is a pioneer in reporting the results of applying CNN to classify Urdu handwritten characters with exceptions. Our contribution in this work is to prove the fact that deep CNN does not require the knowledge of each character individually when trained on the large-scale dataset. The advantage of our proposed recognition system is quite useful to help the children who are learning to write Urdu characters and numerals.