# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Classification Techniques for Scene Text Recognition
Support Vector Machine (SVM): This is another machine learning approach that is used to classify the data by performing regression analysis using supervised learning methods. In a survey paper presented by [PERSON] et al. [35], the work on SVM is compiled and depicted good results on global sampling. The Char74K and ICDAR2003 datasets were used to evaluate their proposed methodology. All experimentation was performed by the SVM classifier [71]. The reported accuracy was 62% on the CHARS74K-15 dataset and 76% on the ICDAR2003 CH dataset using global HOG as a feature vector, while on local HOG, the reported accuracies were 58% and 75% on the above-mentioned datasets, respectively. The work proposed by [PERSON] et al. [72] also trained the SVM classifier on their proposed dataset. They evaluated the performance on the Char74K dataset by following the protocol defined by [PERSON] et.al [54]. The classifier was trained on 7705 annotated characters extracted from 636 images. They also created a language model with 1000 frequently-used English words. They evaluated the character recognition by considering three situations, i.e., a character has been localized and recognized correctly, so the given character is matched; the second situation is when a character has been localized correctly, but not recognized, which led to the problem of the mismatched case. In other words, when the character was not correctly localized, this meant that the character was not found. They compared their results with [PERSON] et al. [54] and found that their technique produced better results, which was 71.6% in comparison to 54.3%. K-means clustering: This is one of the most prominent and simple unsupervised learning algorithms that classifies the given data through a certain number of clusters. One such work was presented by [62]. They proposed an unsupervised learning algorithm that generates features that were used for classification. They guesstimated the variant of K-means clustering and compared their yielded results with other methods. The input image was normalized by compressing the image into a  
Figure 8: Kannada cursive script including printed and scene text samples. 32 \(\times\) 32 image size and then collecting 8 \(\times\) 8 grayscale patches of images, then applying the statistical preprocessing whitening technique as proposed by [73] to the yielded patches, which helped them to make another dataset. They took a number of patches for the purpose of obtaining the vector of pixels, which was normalized with respect to contrast and brightness, and put them in the dictionary. As they applied the K-means variant of clustering, their classifier learned the set of normalized vectors using the inner product as the similarity measure. They evaluated their ICDAR 2003 dataset using linear SVM and reported character recognition accuracy on three different dataset classes, as further explained in their paper. The highest accuracy they reported was 85.5%. Bayesian classifier: The Bayesian classifier is a probabilistic classifier based on the Bayes theorem, which helps to predict the independent assumption about the features. One such work was presented by [34]. The text candidates were obtained by intersecting the output of the Bayesian classifier and the canny edge map of input image. They assumed that the orientation and size of a font does not have any impact on recognition due to the proposed Bayesian classifier. The connected component method was used to make a bounding box of a text. They evaluated the performance of their proposed technique on their collected dataset named Hua [74] and ICDAR 2003 [55]. Minimum Euclidean distance classifier: This is suitable to compute unknown patterns and predict the decision based on the smallest distance. The proposed work by [PERSON] et al. [58] presented the minimum Euclidean classifier to learn the unknown patterns of Chinese handwritten script recognition. The experimental data contained 3755 frequently-used simplified Chinese characters, which were normalized into 64 \(\times\) 64 pixels.