# Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
## 2 Literature Review  
Handwriting recognition has been around in the field of computer science for almost half a century now. In [1], the oldest techniques that have been in use for character recognition since 1959 are discussed. It originates from the work suggested by [PERSON] in 1968 known as analysis-by-synthesis. This is the basis for syntactic approaches in character recognition. [PERSON] and [PERSON] [2] discuss the advancements in preprocessing techniques when input data ranges from simple handwritten documents to deformed images, or images having viewpoint variation and background clutter. They concluded that applying only one preprocessing technique is never enough to obtain high accuracies, but rather a mixture of preprocessing techniques is applied to obtain reliable results. Basically, two types of recognition systems exist; online systems and offline systems [16; 17]. Online character recognition works when user writes on a special writing surface, computer recognizes it and converts it into codes with respect to time, while offline recognition systems are images or documents fed as input with text written on them and are converted into digital form. Offline recognition works in phases where images are first segmented, cropped, and resized, their features are extracted, and then they are classified [18]. This paper presents work on Urdu numerals using offline recognition. Table 1 presents a summary of the accuracies achieved for other algorithms using Urdu datasets. All these papers used different datasets and since those datasets are not available publicly, it was difficult for us to compare them with our techniques. Additionally, these datasets were collected on different lines and solved various problems, but underlying concept is same that they work on variations of Urdu language datasets; hence, their comparison is necessary. In [23], [PERSON], [PERSON], and [PERSON] state work done on Eastern Arabic numerals through OCR. [PERSON] et al. [6] compared the problems encountered in Latin and Arabic handwritten numerals by using the Arabic Handwritten Digits Database (ADBase) [24]. [PERSON] and [PERSON] proposed a recognition system [19] based on approaches of segmentation for feature extraction, slant analysis for slant removal, and dictionary search for classification. It resulted in a recognition rate of 93% for isolated characters and the same for numerals. In another study [25], [PERSON], [PERSON], and [PERSON] provided an in-depth review of statistical, kernel, artificial neural network (ANN), template matching, and structural methods for classification of OCR. In a very interesting work presented by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] et al. [7], a 6.04-7.93% error rate on \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article Reference** & **Techniques Applied** & **Accuracy Achieved** \\ \hline
[19] & Back propagation neural network & 90\% \\
[8] & Kohonen self-organization maps & 91\% \\
[20] & Shape context-based digit recognition computation & 93\% \\
[21] & Fuzzy rule & 97.4\% \\
[22] & Capsule-Net & 98.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy reported on different techniques. 700 unique text lines was achieved (including Urdu numerals and Urdu handwritten samples) after applying 1-D bidirectional long short-term memory (BLSTM) networks. In [8], [PERSON], [PERSON], [PERSON], and [PERSON] presented the utilization of Kohonen self-organization maps on 6000 handwritten Urdu numerals and obtained an efficiency of 91%. A work similar to this paper was conducted by [PERSON] in [7], where Urdu text was integrated with the modified National Institute of Standards and Technology dataset (MNIST) to learn the similar nature of patterns. They used CNN and multidimensional long short-term memory (MDLSTM) on UNHD samples by pretraining the network on MNIST. Their results showed the highest recall of 0.84 and 0.93 for precision.