# Arabic Cursive Text Recognition from Natural Scene Images
## 4 Discussion  
This survey summarized the latest trends and techniques that have been applied to cursive scene text recognition, specifically in Arabic script. This section elaborate how the discussed approaches can assist in text detection and recognition of Arabic text appearing in natural images. The prime concern in scene text images is to localize the text in the presence of non-textual patterns. In this survey, the latest trends that were specifically designed for text localization and classification with state-of-the-art techniques are discussed. The pre-processing of an image is an initial, but essential step, which is considered before applying text localization techniques. The pre-processing requires a homogeneous size and display of an image later, and text localization techniques can be applied to given standardized scene text images. It was learned from the literature that on the basis of scene text complexity, the learning process can further be divided into three major phases (i.e., text localization, feature extraction and text recognition). As noticed in this survey paper, most of the techniques were presented for Latin scene text segmentation, but if one applies these techniques to Arabic script, then it would not produce the expected results. The main reason for this variance in results is the difference of the text's appearance in both scripts. The reported work on Arabic scene text analysis (as mentioned in the previous section) used manual segmentation by keeping in view the complex structure of Arabic because of the cursive complexity associated with this script in order to obtain high precision in recognition. The subsequent phase after text localization is feature extraction. The feature extraction is meant to observe distinctive properties that exist in the provided sample. In machine learning tasks, the features behave as a backbone for recognition purposes. In this manuscript, the feature extraction approaches defined for cursive scene text recognition are discussed. Being a cursive language and having a complex structure, every character in Arabic has four representations based on its position in a word. At each position, the representation of the character is different. With this constraint, the standardized segmentation approaches may not perform well. In Arabic, the context of character is very important to learn. There is a need to know the previous character so that the next character can be predicted in a word. In this way, sequence learning becomes an integral part of Arabic script analysis. Every acquired text image has built-in features associated with it. These features can be categorized as a unique attribute of an image. As in the feature extraction phase, there is a need to investigate each scene text image so that proper features may be classified during learning. In Arabic, the context is crucial to learn and also to be a part of the given sample during the feature extraction phase. In this paper, some important feature extraction techniques are summarized like global sampling, histogram gradients, scale-invariant transformation and hybrid feature extraction approaches. Among the discussed approaches, SIFT is more relevant to Arabic scene text feature extraction proposed by most researchers with little adaptation. Another very important feature learning approach is window based. By declaring the size of a window in an image, all pixels of an image are considered as a feature regardless of individual character representation. By doing so, the contextual representation of Arabic scene text images is considered. The extracted features with the corresponding text image are presented to the classifier. The learning of the classification model can be defined as supervised and unsupervised. Most of the