# Arabic Cursive Text Recognition from Natural Scene Images
## 1 Introduction  
Advancement in cameras of hand-held gadgets prompt their users to capture scene images having overlaid text. In today's era, most people have specialized gadgets to capture scene images for obtaining information during work, a journey, etc. The camera-captured images may contain much textual information in addition to semantic knowledge represented by graphics or in pictures. The text appearing in natural images is usually used for conveying information to people. The scene text is represented by different types of font styles and sizes having various backgrounds, including building, sea, mountain, forest, etc., which are termed as noise and may halt the smooth process of text recognition from natural images. The natural images having text can be seen on signboards, banners and advertising notes or boards. The text extraction from a natural image is an emerging research field as far as cursive scene text recognition is concerned. This problem is noted to be a challenging one due to implicit noise like blur, lighting condition, text alignment, styles, orientation and the size of textattached to the image. Moreover, a complex background sometimes makes it difficult to extract the text in comparison with traditional Optical Character Recognition (OCR). The extracted text from natural scene images is beneficial for applications such as text search in a video, text extraction from videos, content-based retrieval, search engines and in those applications where text in an image has an important concern. The focus of this paper remains on the investigation and analysis of state-of-the-art techniques developed for text extraction and recognition of textual data as captured in an image. This paper particularly emphasizes the work presented in recently-proposed research on cursive text, especially Arabic scene text recognition. A cursive word is more related to and used with script, which means any penmanship style by using the various symbols of any language that is written in a conjoined and flowing way. The text other than printed Latin may be taken from different writing styles. There are numerous complicated scripts that exist, like in Arabic, Chinese, and Japanese, that are categorized as cursive in nature, either taken by a specialized camera, synthetic means or in handwritten form. Plain or Latin text recognition is no longer a research problem as many researchers have proposed a solution for efficient printed, handwritten and scene text recognition for Latin [1; 2; 3; 4] systems. However, the recognition of non-Latin scripts still poses a great challenge and requires more effort from the research community to address it. A non-Latin and prominent cursive script is Arabic. In Arabic, the text is cursive in nature because isolated characters do not represent any meaning unless they are used in conjunction with other characters, as presented in Figure 1.  
There are four positions of a character in a word. These positions may be initial, middle, final or as an isolated character. Due to various appearances of the same character, the segmentation becomes very difficult to perform. For this reason, implicit segmentation approaches are presented to handle the segmentation problem [4; 5]. The following are the highlighted issues related to Arabic-like cursive scripts. 1. More than one shape of a character increases the complexity in character recognition. 2. The diacritics on a character are a more important feature of some characters in Arabic, because without using them, it becomes difficult to read a proper word and understand its meaning. 3. Contrary to Latin, the writing style is from right to left. In recent years, the research work based on implicit segmentation and context learning classifiers on Arabic/Urdu script OCRs either in printed or in handwritten form have been described in [4; 5; 6]. The state-of-the-art technique like Recurrent Neural Networks (RNNs) [7] has been applied to Urdu cursive script and resulted in achieving remarkable accuracies as reported on this intrinsic script in [4; 5; 8].