# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 3 Literature Review
### Urdu Handwritten Character Recognition
Some of the stroke features that were used to train the classifier were as follows: the diagonal length of the bounding box; the sine-cosine angle ration of the bounding box diagonal; the displacement of the first and last point while tracing the bounding box; the corresponding sine-cosine ratio of the angle between the first and last point; the total length (in pixels) of the primary stroke; and the total angle traversed. A linear classifier was applied to the dataset of five samples each of 38 Urdu characters, i.e., a total of 190 characters were provided by two different writers who could write Urdu characters smoothly. The classifier recognized the characters with an error rate of almost 6% because some characters share quite similar shapes (see Figure 5) and were not correctly recognized. Similar work was reported in [23] by considering the initial half of different Urdu characters. In this work, only those characters were considered that change their shapes concerning their position and context in a word. Figure 6 depicts Urdu characters in the initial half forms and classified based on the number of strokes. Almost 100 native Urdu writers and speakers were invited to write in Urdu script. The writers were provided with a stylus and digitizing tablet to get the dataset of 3600 instances of Urdu letters in the initial half form. A combination of multilevel one-dimensional wavelet analysis with the Daubechies wavelet [34; 35; 36] was applied to extract features from these instances. Several neural networks with different configurations were trained for recognition purposes. Among these networks, BPNN provided a maximum recognition rate of 92%. The MDLSTM (multidimensional long short-term memory) neural network is one of the RNNs (recurrence neural networks) that is implicitly used for sequence learning and segmentation in multidimensional environments [37; 38; 39]. This model was used for the first time in the work of [26] for Urdu script recognition. One of the promising features of the model is that it can scan the input image in all four directions, thus reducing the chance of ambiguity. For evaluation purposes, the UPTI (Urdu Printed Text Image) dataset [40] was used, which contains 10,000 scanned images of both Urdu handwritten and printed text. MDLSTM is one of the supervised techniques; therefore, each input sample in the dataset is tagged and labeled with appropriate information. The dataset is further divided according to the following ratio: 68% for training and 16% for both testing and validation purposes. In order to evaluate the accuracy of the proposed approach, the Levenshtein edit distance [41] was computed between the output text and baseline results and achieved an accuracy of 94.04% as compared to the results reported in the works of [42; 43], reporting 88.94% and 89% accuracy, respectively. Table 1 shows a comparison of the proposed approach on the UPTI dataset [40] with other techniques. Figure 5: Urdu character “De” (**left**) and “Daal” (**right**) were repeatedly misrecognized. Promising work was reported in [46] in which Urdu handwritten text was recognized using the dataset UNHD (Urdu Nastaliq Handwritten Dataset) [47]. This dataset can be accessed publicly [[https://sites.google.com/site/researchUrdulanguage1/databases](https://sites.google.com/site/researchUrdulanguage1/databases)]([https://sites.google.com/site/researchUrdulanguage1/databases](https://sites.google.com/site/researchUrdulanguage1/databases)) UNHD Database. The dataset contains 312,000 words (including both Urdu script and Urdu numerals) written on a total of 10,000 lines by 500 writers of different age groups. The writers were directed to write on white pages of size A4. Each was provided six blank pages labeled with the author ID and the page number. One of the samples of written pages is shown in Figure 7. Furthermore, in order to maintain the uniformity in data, the writers were asked to write the provided printed text.