# 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
All Chinese images were normalized to x-height 13-365 pixels, while width was adjusted from 11-325 pixels, as explained in their research. They also mentioned their evaluated results on the English dataset. Their best result was reported on convolutional Co-HoG (Histogram of Gradients)  
Maximally-Stable Extremal Regions (MSER): In computer vision, this is a well-known method for blob detection. It was first introduced by [PERSON] et al. [43]. The correspondence between two image elements with two different view points was considered. The assumption was to extract a comprehensive number of image elements that contribute to matching the baseline, which helps with the detection of an object in an image. This algorithm has been applied to detect text candidates in various state-of-the-art applications [44; 45; 46; 47]. A multilingual text detection through MSER was proposed by [48]. They used MSER to detect text from the scene image. The input for the MSERalgorithm is a greyscale image \(I_{g}\), and the output is \(I_{t}\), where \(t=0\)-255. They first binarized the image with a threshold \(t\). Every pixel was evaluated and changed into a black or a white area, where zero means completely black and 255 completely white. The white area in the image is called the extremal region. To detect extremal regions, the rest of the pixel area should be the same. The threshold \(T\) was applied to take the exact number of interested regions. By applying threshold \(T\) over the image, they obtained successive regions that were not impacted by the overall process. Such regions are said to be Maximally-Stable Extremal Regions (MSER). In another manuscript, the candidate regions were detected by MSER [22]. As the nature of MSER suggests, the individual characters are not detected correctly by it because most of the times, it is used to find the region of interest. Hence, it has been proven to be suitable to find the number of characters or words. In general, MSERs in an image is categorized into three classes. The first class corresponds to individual characters, while the second class may have an arbitrary number of characters, whereas the third class may contain all non-textual background content. They estimated characters on the basis of character strokes. The character area was expanded by calculating the distance transform map, which depends on the basis of the calculated binary mask. Here, the pixels played an important role, and local maximum distance was considered for extremal region detection. For the purpose of calculating character stroke area \(A_{s}\)[22], the following equation is used,  
\[A_{s}=2\sum_{i\in S}d_{i} \tag{2}\]  
where \(S\) is the stroke and \(d_{i}\) is the distance of pixel \(i\) to the boundary. Their proposed estimation was taken as correct only for the strokes that have an odd width, while it became inaccurate on even widths. The boundaries of stroke pixels were not connected to each other because of this noise. This has been compensated by introducing weight \(w_{i}\) as follows,  
\[A_{s}=2\sum_{i\in S}w_{i}d_{i},\ \ \ w_{i}=\frac{3}{|N_{i}|} \tag{3}\]  
where \(N_{i}\) denotes the number of stroke pixels in the \(3\times 3\) matrix. Another very interesting manuscript describing MSER was proposed by [47]. They exploited the characteristics of MSER to detect text from given images. For efficient processing, they predicted extremal regions in an image and took computational complexity into consideration. The search space was also limited by linear timing. After examining numerous manuscripts describing the use of MSER, it is observed that MSER is a very important and suitable technique to detect character candidates from a scene image. It is also considered as an invariant to affine transformation. By keeping its ability to search the point of interest in the provided area, it can produce good results with low quality images. The implementation complexity is \(O(nlog(log(n)))\), where \(n\) is the number of pixels in an image.