# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Methods Designed for Feature Extraction  
In the context of machine learning and pattern recognition applications, features are considered as the backbone of any recognition system. In this sub-section, the importance of feature extraction techniques by providing the analysis of recently-proposed methods reporting good accuracy is presented. The feature values derived from the raw or initial set of given data intend discriminative and non-redundant data. This discriminative and non-redundant data facilitate the process of further classification and learning steps. There are numerous feature extraction methods proposed by various researchers designed specifically for scene text images. The detail about prominent methods is depicted in the following paragraphs. Global sampling: As described in [35], the scene text character's recognition performance is evaluated by considering the comparison of different sampling methods, i.e., local and global sampling, feature descriptors [16; 50; 51; 52], dictionary sizes, coding and pooling schemes [53] and Support Vector Machine (SVM) kernels [15]. To obtain the features from local sampling as mentioned in [35], the keypoint detection, compute local descriptors, build dictionary of visual words and feature pooling and coding to get the histogram of visual words are important to investigate due to their discriminative nature. They computed the descriptors from a character that is patched by global sampling without considering keypoints, local descriptors, coding and pooling. The features they found by following their process are regarded as distinct features that are ready for classification. Multiscale histogram gradient descriptors: The multiscale histogram of oriented gradient descriptors as a feature vector was reported by [10]. They included features at multiple scales in a column-wise manner of the HOG descriptor and evaluated the performance on Latin scene text images having variation of characters. The oriented gradients were calculated using the derivative of Gaussian filters. Then, the total strength of each variation was summed up, and this process continued on each block in an image. Each histogram with respect to each block was normalized so as to sum up the total variation across all orientations. In this way, they made a single descriptor for each image. They evaluated their proposed technique on two commonly-used datasets, i.e., Chars74K [54] and ICDAR03-CH [55]. Chars74k contains 62 classes, which include number, upper and lower case characters. The other dataset they used was ICDAR03-CH, which is the character recognition dataset that was presented at the robust reading competition in ICDAR2003. This dataset is similar to Chars74k. In addition, it included the images of punctuation symbols. They split the Chars74k dataset into Chars74k-5 and Chars74k-15 training images per class. The ICDAR03-CH-5 dataset was used with four training images per class. The reported accuracy using multiscale HOG was 50%, 60% and 49% and by HOG columns was 59%, 67% and 58% on Chars74k-5, Chars74k-15 and ICDAR03-CH-5, respectively. Scale-Invariant Feature Transformation (SIFT): This is a main computer vision algorithm that is used to define the local features of an image. The local features that it detects are the keypoints that are not affected by image transformation. The extracted keypoints from the SIFT algorithm are depicted in Figure 6. The scale-invariant approach is applied to Arabic scene text recognition [31] with the combination of sparse coding [56] and spatial pyramid matching [57], as shown in Figure 7.  
They extracted the local features from SIFT [38], which is considered as a very efficient technique that demonstrates and extracts the most relevant distinguished local features. In order to get more precise information of an image, the weighted linear super-position function is applied to extracted descriptors. The input image was divided into sub-regions, then features relevant to a specific sub-region using different scales were modelled into the histogram.