# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 3 Literature Review
### Urdu Numerical Recognition  
It is quite easy for a human being to recognize the handwritten numeral data, but for the computer system, there is a need for an intelligent approach based on some machine learning algorithms developed for this kind of job. The writing stroke, length, width, orientation, and other geometrical features tend to change while writing the same numeral even by the same author. These different writing styles may introduce shape variations of Urdu numerals that may break the strokes' primitives and also change their topology. These issues make Urdu handwritten numeral recognition one of the active research areas in the field of image processing. Unfortunately, there is no commercially-available standard dataset of Urdu numerals. Due to this lack of resources, the researchers developed their own dataset and concluded the results. This section covers some notable work related to handwritten numeral recognition in the Urdu domain. In [51], different transformations of the Daubechies wavelet [34; 35; 36] were applied for feature extraction from a dataset of about 2150 samples of handwritten Urdu numerals. For evaluation purposes, 2000 samples were used for training the neural network and 150 instances for testing. In order to decompose the images into different frequency bands, both the low-pass and high-pass filtering were applied at each phase of the Daubechies wavelet [34; 35; 36] filtering. For classification purposes, BPNN was used and achieved an average recognition rate of 92.05%, as shown in Figure 8.  
In [52; 53], the authors presented the similarities and dissimilarities between Urdu and Arabic script with recognition of handwritten numeric data. A hybrid technique of HMM and the fuzzy rule was used to recognize the handwritten numerals of both Arabic and Urdu script. The dataset was prepared by inviting 30 trained users to write both the Urdu and Arabic numerals and collected 900 samples in total. The system obtained 97%, 96%, and 97.8% recognition rates using the fuzzy rule, HMM, and the hybrid approach, respectively. The authors also conclude that separation of numerals from Urdu text in a handwritten text is still a challenging issue due to having shape similarity, e.g., First character of Urdu script (Alif) and Urdu numeric (One) both have exactly same shape. A new algorithm is proposed in [54] to preprocess the complex input and preserve shape of the actual input. Fuzzy association rules are used to link secondary stroke with their respective primary strokes. Different classifiers such as the hidden Markov model (HMM), fuzzy logic, the \(k\)-nearest neighbor (KNN), hybrid fuzzy HMM, hybrid KNN fuzzy, and the convolutional neural network (CNN) wee used for the classification. Statistical tests were applied to find the significance of classifiers' results. Similarly, a newly-developed OCR algorithm was introduced in the work reported in [55] that used a semi-supervised multi-level clustering for categorization of the ligatures. Classification was performed using four machine learning techniques, i.e., decision trees, linear discriminant analysis, naive Bayes,  
Figure 8: Recognition rate and recognition time using different Daubechies wavelets for handwritten numerals [51]. and k-nearest neighbor (\(k\)-NN). The system was implemented, and the results showed 62, 61, 73, and 9% accuracy for the decision tree, linear discriminant analysis, naive Bayes, and \(k\)-NN, respectively. In a very recent work [56], the authors presented a simple and robust line segmentation algorithm for Urdu handwritten and printed text. In the proposed line segmentation algorithm, a modified header and a baseline detection method were used. This technique purely depends on the counting pixels approach, which efficiently segments Urdu handwritten and printed text lines along with skew detection. The handwritten and printed Urdu text dataset was manually generated for evaluating the algorithm.