# Arabic Cursive Text Recognition from Natural Scene Images
## 1 Introduction
However, it may be categorized as a problem of the OCR field that contemplates different approaches to recognize scene text. The accuracies reported on various OCR techniques home scene text images in the presence of non-text patterns. However, scene text recognition is labelled as a specialized problem of OCR, but there are distinct issues relevant to scene text recognition in comparison to typical OCR systems. One of the prominent issues in scene text is localization of a text from natural scene images. There are numerous techniques presented to address text localization as explained in [21; 22; 23; 24] and text classification as examined in [25; 26; 27; 28], respectively. This survey summarizes recent techniques designed for both phases, i.e., feature extraction and classification in scene text recognition. Active research contributions on scene text have been witnessed during past few years. Primarily, the proposed techniques have been applied to localization and recognition of Latin text from natural images [9; 10; 14]. The cursive script postulates more of a challenge to recognize text from the acquired image. As mentioned earlier, Arabic script is cursive in nature due to the inherent variability of single and joining characters [4; 29]. Figure 3 shows the Arabic and Latin camera-captured scene text. In recent years, a few research works have been reported [30; 31; 32] on Arabic text recognition especially in video images, but there is no standard dataset available for the aforementioned purpose. The work reporting on Arabic scene text is relevant to the images clipped from videos, as explained in [33; 34; 32]. Today, due to advanced media in every country, there are numerous international, national and regional news telecasts by each country. Thus, video text is easier to get in comparison to the proposed camera-captured scene text images. The text represented in a news video can be categorized into two types, i.e., artificial text and scene text. The text that is artificially overlaid on a video image is treated as prior, whereas the text image taken from camera during video capturing is classified as later. It is obvious that OCR will not directly process the video image because the nature of OCR is more towards processing clean document images taken at standard resolution and in specific settings. The video images often have colour blending, blur, low resolution and complicated backgrounds in the presence of different objects. This paper presents a comprehensive literature survey on cursive scripts, especially Arabic scene text recognition. The most influential and convincing work proposed in recent years is summarized in this paper. The state-of-the-art techniques for text detection have mainly been summarized by [35]. Another survey was compiled by [23] to discuss detection and recognition techniques, but it lacked recent state-of-the-art techniques. A very persuasive survey paper was written by [23]. They mostly  
Figure 3: Cursive and non-cursive captured text image. discussed the latest techniques that were established in recent years for text detection and recognition, but with respect to Latin. Furthermore, they also discussed future trends. The motivation behind the contribution of this survey is as follows:  
1. Scattered works have been seen on cursive scene text recognition, especially in Arabic script. The aim is to provide detailed knowledge to researchers about the current status of work for the purpose of discussing the future possibilities of research in Arabic scene text analysis. 2. This survey provides a substantial contribution from the researcher's point of view to address the inherent complexities as explained in [4; 5; 8; 30] and proposes an idea of how to overcome them. 3. The details about state-of-the-art techniques are also provided, which exhibit the research on cursive scene text recognition and may assist Arabic text recognition in natural images. 4.