# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Methods Designed for Text Localization
The weighted average was used to determine the candidate cluster. A template of a window size of \(5\times 7\) was suggested to  
Figure 5: Scale-based region growing with the identification of keypoints and marking of a text. store the stroke information of each character. They used 63 classes, which were divided into 10 Arabic numerals and 52 English letters and one special class to handle the appearance of invalid characters. The evaluation was performed on two publicly-available datasets, i.e., dataset defined in International Conference of Document Analysis and Recognition dataset ICDAR2003 and Street View Text (SVT) [40]. Text detector based on the Laplacian and Sobel filter: The orientation of text in video images portrays a challenge for researchers due to the constraint of the size and orientation of a text image. To address the problems and challenges in multi-orientation in video text images, [PERSON] et al. [34] proposed a text detection approach. They applied the product of the Laplacian and Sobel filter for enhancement of pixel values. The discontinuities in an image gave them a strong indication about the presence of text. They used a \(3\times 3\) mask to get the enhanced text pixels. The Laplacian is a second order derivative and is used to detect dissimilarities in four directions, i.e., horizontal, vertical, up left and up right. In this way, information of low or high contrast images was enhanced. However, the results they found were noisy. For the purpose of handling this noise, they used the product of the Laplacian and Sobel filter. The Sobel filter is a first derivative, and it produces a fine detail at discontinuities in the horizontal and vertical directions. They first individually applied both techniques and later combined the result as a product of both techniques. Connected component analysis: This is a labelling technique that scans an image and groups its candidate pixels in a way that all connected pixels make a component that shares similar intensity values and some how their pixels get connected to each other. This technique is applied to natural images or to video text images for determination of text area. The video image often has a complex background, cluttered text and a jerking image. In this situation, it becomes very difficult for any type of method to produce encouraging results. The text localization using the stroke filter in video images was proposed by [41]. They designed a stroke filter in such a way that uses the properties of local feature analysis and global constraints. They employed connected component analysis for text localization in text regions. They applied the stroke filter on the source image and later analysed it by the connected component analysis technique. They compared the stroke filter with Cannoy, Gabor, Haar and ratio filters and came to the conclusion that the stroke filter was best among them. Co-occurrence of the histogram of oriented gradients: Another cursive scene text detection and recognition work was presented by [42]. They proposed scene character recognition using the co-occurrence of the histogram having oriented gradients. The information about the oriented gradient of the image pixel is a point of interest, while ignoring its neighbouring pixel's values. The relevant pixels were selected based on the maximum offset represented in the horizontal and vertical directions. In this way, pixels made an orientation pair with reference to the central pixel, and covariance was accumulated at the end. The elaborated description of said approach may be found in their paper. They evaluated their technique on Latin, Bengali and Chinese script. They captured 260 Bengali scene text images. On the other hand, they collected 487 Chinese scene text images, and out of them, 3419 were used for training and 2763 for testing purposes. They also evaluated their proposed method on a Chinese dataset named ChiPhoto, which consisted of 343 scene text images. The ChiPhoto dataset includes classes that are not included in the Pan_Chinese_Character dataset.