# Arabic Cursive Text Recognition from Natural Scene Images
## 1 Introduction
Although the work on optical character recognition of Urdu-Arabic-like scripts tried to present commendable solutions as shown in recent research publication on this subject, the recognition  
Figure 1: Arabic as cursive script. The green box shows different positions of the Arabic character noon, while the blue box represents different positions of the Arabic character laam. The red box depicts various positions of the Arabic character seen. of Arabic scene text has not shown significant results yet. As discussed earlier, the techniques that have been applied to OCR systems have failed catastrophically on Arabic scene text recognition. This is due to the complex structure of Arabic scene text images in the presence of varied styles of text represented in any colour and in any orientation regardless of following any font style and size. Efforts are being made to overcome the difficulties in this direction, and some research work has been reported, which will be detailed later in this article. There is supplementary inter- and intra-class variation in the text extracted from natural images. It is comparatively easier to recognize Latin script from natural images, unlike Arabic, Chinese, Japanese or any other cursive script. Figure 2 shows scene text images of different languages. This survey summarizes the work of other researchers who have contributed to cursive scene text recognition since 2009 to date. In addition to that, the status of Arabic or Arabic-like scripts is also accentuated. Arabic script is one of the most common and the second largest language, having the status of the national languages of the Arabian Peninsula. Around more than one billion users in the world communicate in the Arabic script-based languages in reading and writing. The writing style of Arabic script is from right to left with the combination of diacritics, which is considered an integral part in making a word more meaningful. There is a variety of techniques presented for Arabic or Arabic-like text recognition, either in printed [8], scanned or handwritten format [6]. The existing methods designed for scene text detection and recognition may be categorized into texture-based, component-based and hybrid-based methods. 1. The texture-based method relies on the properties of an image like intensity and hue values, wavelet transformation of an image and by applying different filtration techniques, which contribute to representing the image. Such properties may help to detect the text in an image as explained in some of the presented work, like [9; 10; 11; 12]. 2. The component-based method depends on the specific region(s) of an image. The region is often marked by colour clustering and coordinate values. Different filtration techniques may be applied to segment the text and non-text region from an image. If scene text images are taken in specific settings, then component-based methods produce good results. This method is not suitable for invariant text images like the difference in font size, rotation, etc. Some researchers proposed their techniques by using this method, as mentioned by the researchers in [4; 5; 7; 13; 14]. 3. The hybrid methods share the characteristics of both texture-based and component-based methods. The candidate regions is determined by using both techniques on the same image, as explored in some works, as mentioned in [15; 16; 17; 18]. The text segmentation approaches that have been applied to OCR systems can also be applied to scene text recognition systems, as reported by [19; 20]. These approaches produced state-of-the-art  
Figure 2: Multilingual scene text images. The text in the images is representative of Chinese, Hindi, Urdu, English and Tamil. results on OCR either being applied to cursive or to non-cursive scripts. The text in natural images and that in printed or scanned documents do not share any commonalities. That is why these approaches drastically failed on scene text recognition systems. Numerous ideas have been proposed to address the complexities involved in scene text recognition.