# Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
## 1 Introduction  
OCR technology scans printed characters to determine their shape by recognizing edge information, and then translates them into characters by process of character recognition [1]. In recent years, one of the most fascinating and difficult research areas in the fields of image processing and pattern recognition has been handwriting recognition. It has many applications such as OCR, pattern classification, postal mail sorting, bank cheque processing, form data entry, etc. Such character recognizers prove to be fruitful for humans because of their speed and accuracy. Mostly, they are based on deep learning models and solve the problem efficiently. In the study of languages, Urdu is one of those cursive languages that is hugely popular in South Asian countries such as Pakistan, India, Bangladesh, Bhutan, and Nepal. It is the national language of Pakistan and is widely spoken in urban areas, while its adoption as a second language in rural areas is in progress. It is an amalgamation of many languages; hence, its script contains loanwords and is written in different variants all around the globe. Another important characteristic is that it is bidirectional with its numerals written from left to right while its script is written in the opposite direction [2]; therefore, this becomes a problem for OCR. It has up to 40 letters in its script and 10 numerals [3]. Urdu, Persian, and Eastern Arabic numerals are written on similar patterns; however, some of the digits differ, as shown in Figure 1, which is yet again another concern. Some other challenges thatare encountered during its OCR is blurred text, torn paper, and spacing between letters when the data is found in written form [3]. Our aim is to develop a classifier for our novel dataset. The motivation behind developing an Urdu numeral classifier is that majority of the work has been done on English numerals; however, no such work has been done on Urdu numerals which are distinct in features as compared to English. Data unavailability is a major obstacle in the development of Urdu handwritten character recognition [4; 5]. As no dataset of Urdu numerals is available publicly for research purposes, we, therefore, present a novel dataset gathered specifically for this study. In this way, problems related to Urdu character recognition, OCR, and intelligent character recognition (ICR) can be solved as efficiently as they are solved in other languages. The previous datasets are of Latin, Arabic handwritten numerals [6], text lines of Urdu [7], integration of MNIST with Urdu numerals [8], Persian numerals [9], Bengali numerals [10], Urdu Nastalique Handwritten Dataset (UNHD) [11], Urdu Printed Text Image Database (UPTI) [12], and Urdu spoken and text words [13]. We did come across an Urdu numerals' dataset by [PERSON] et al. [14], but that did not incorporate variations of crumbled, torn, and ink spots on paper. So, we sought to create our novel dataset of 9800 images of handwritten Urdu numerals written by over 200 individuals with their left and right hands. The difficulty of accurately classifying handwritten characters is increased by variances in writing style, character size and form, and resemblances to other characters [5]. Hence these papers were then crumbled and torn, and some had ink spots added on them for variety. The pictures of these images were preprocessed by employing a Gaussian filter and connected component labeling. These images were then fed to machine learning and deep learning models for classification purpose. Deep learning models find out complex structures in massive datasets by using the backpropagation algorithm to indicate how a machine can change its internal parameters in each layer from representation in the previous layer [15]. We applied our proposed CNN, GoogLeNet, ResNet, and SVM on the dataset. Our proposed solution is powerful, yet simple, and results in a performance which is comparable or higher than the state-of-the-art when evaluated on our novel dataset.