# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 3 Literature Review
### Urdu Handwritten Character Recognition  
Handwritten text recognition at the character level is a challenging task because of having a large number of variations in writing styles (even from a single author). It is observed from the literature related to character-level recognition in the Urdu script, the artificial neural network (ANN) and its different variants are widely used. An ANN [32] is a collection of nodes (also known as artificial neurons) linked with each other. These links between artificial neurons are enabled to transmit a signal from one to another within the network. These neurons can process the signals received and then propagate to the neurons connected in subsequent layers. The structure of the ANN may be affected by the kind of information flowing through it because a neural network usually trains itself using the input and labeled output. The problem of developing a generic type of ICR that can resolve the issues associated with any language is challenging since different languages exhibit different characteristic features, and thus, generalizing this type of system is not possible. In order to overcome this problem, a novel approach was proposed in [33] exploring how the character set of any language can be represented by primitive geometrical strokes. One of the promising features of the approach is that the recognizer (artificial neural network) has to be trained only once. The data structure of the character set should be represented in the form of geometrical strokes in an XML file. This file helps in training the neural network, not for every time, for each word in the language. Figure 2 shows a set of thirteen basic geometrical strokes. For evaluation purposes, a set of 25 handwritten Urdu text samples were tested and achieved a success rate of 75-80%. One of the limitations of this approach is that it does not apply to the words having dots and diacritics. Figure 1: Urdu character set with phonemes and numerals with their Roman equivalences. Due to having a large character (or alphabet) set, there is inherent similarity among some major strokes, as shown in Figure 2. This similarity in characters is one of the challenging issues of the incorrect recognition of Urdu handwritten text. Keeping in view the fact mentioned above, in [21], the authors divided the Urdu character set into four groups according to the number of strokes, as shown in Figure 3. The authors performed an online Urdu ICR considering single-stroke characters only. Some novel features (shown in Figure 4) were extracted and then fed to three different classifiers namely, the back propagation neural network (BPNN), the probabilistic neural network (PNN), and the correlation-based classifier. The proposed approach was tested on 85 instances of single-stroke characters taken from 35 writers of different age groups. The results showed that the PNN classifier achieved a higher accuracy of 95% as compared to the other two classifiers. Unlike BNN, the PNN-based classifiers require no initial training. This is the reason PNN-based classifiers achieved higher accuracy than BNN. Figure 4: Statistical features: (**a**) common trend while writing Urdu character “[PERSON]”; (**b**) slope value of the character-box for Urdu character “[PERSON]”; (**c**) identification of a cusp in Urdu character “[PERSON]”; (**d**) feature vector having intersection points of Urdu character “[PERSON]” (U: up, D: down, R: right, L: left); (**e**) finishing trend of Urdu character “[PERSON]” [21]. Figure 3: The Urdu characters: (**a**) the matching order of pen-strokes among Urdu characters; (**b**) groups of Urdu isolated characters based on the number of strokes [21]. Figure 2: Basic geometrical strokes of Urdu script [33]. For isolated character recognition, the authors in [22] proposed a technique that builds the feature vector by analyzing the primary and secondary strokes while writing Urdu characters in isolated form.