# 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  
Text detection or localization is regarded as an important part of information extraction systems. During recent years, novel approaches for text detection have been proposed by the pattern recognition, document image analysis and computer vision research communities [36]. In most of the surveyed work, it is observed that the script identification in multi-script scene text images is presented as a prominent focal point of current researcher's interest. The text detection and localization algorithms are not designed for a specific language. Instead, this process is the same for any type of language, but the recognition techniques may vary depending on the nature of the script's characteristics and  
Figure 4: Scene Text Recognition (STR) phases. complexity. Numerous methods have been reported in the past few years that describe the correct localization of text in natural images. Some of them have been explained in the following paragraphs with their reference. Conditional Random Fields (CRF): This is a probabilistic method that predicts the sequence of missing information between the previous and current sequence of content appearing in order to make an exact or approximate label. One such work was presented by [37]: they proposed a hybrid approach for text localization, which was based on CRF. Their proposed method consisted of three stages, i.e., pre-processing, connected component analysis and minimum classification error. The connected component technique may lead to the problem of inaccurate localization of text; hence, CRF is employed to predict the accurate label. Scale-based region growing: Text detection based on region growing was proposed by [11]. The process of region growing starts from the keypoints detected by the Scale-Invariant Feature Transformation (SIFT) algorithm [38]. By the SIFT algorithm, the keypoints of a given image were extracted, which is treated as a feature regardless of extracting the keypoints that appear in a text or a non-text region. Their algorithm defined the region's range and summed up all keypoints that fall in the range specified for the region; in this way, the region gradually grows larger. The extracted text blocks map geometrically, which sometimes includes background lines as a candidate region, which might decrease the OCR accuracy. To address this problem, they proposed a fast text block division algorithm, the detail of which can be seen in their manuscript [11]. Figure 5 shows the scale-based region growing with the identification of keypoints. Oriented stroke detection: The text stroke orientation may also play a vital role in scene text detection, as presented by [14]. The assumption lies in the fact that every character is represented by its stroke information. They measured the orientation of the character stroke. For instance, character \"_A_\" is represented by two stroke directions, one that is 60 degrees on the left and the other 180 degree on right, and both are joined on the top. One stroke makes zero degrees, which joins the left and right stroke from the middle. The gradient projection \"G\" of every character is modelled and noted down direction \(\alpha\) with a scale \"\(s\)\", as described in their paper as follows,  
\[G_{\alpha,s}=\frac{\beta R_{\alpha}S_{s}I}{\beta} \tag{1}\]  
\(R_{\alpha}\) is represented as a rotation matrix of an angle \(\alpha\); whereas, \(S_{s}\) is labelled as the scaling matrix of a scale \(s\). The detail about their proposed method can be seen in the manuscript [14]. The candidate region is detected, and it grows by detecting at least a single stroke in an image \(I\). Another work based on strokelets to detect multilingual text from scene images was proposed by [39]. The stroke information of every character has been taken into account and compared with the rest of the text for character recognition. The overall process was to find the centre of a candidate character by seeking maxima in the Hough transform using mean shift.