# Arabic Cursive Text Recognition from Natural Scene Images
## 5 Future Directions  
The state-of-the-art techniques that have been applied to cursive scene text recognition are summarized in this survey. The emphasis is to have an idea about how research can be performed on recognition of Arabic script appearing in natural images. Future challenges are identified by keeping Arabic script in perspective. Some possible future directions may include:  
1. Lack of publicly-available Arabic scene text dataset: Capture and compiling Arabic script scene images is an old challenge itself. The text in natural images usually appears with blur, shudder and at low resolution. Taking an ideal image for research purposes is a provocative task. Although few Arabic scene text datasets are available as reported in [87], to define a detailed dataset in which natural images have text captured in the presence of illumination variation, different text sizes and font styles, there is a need for research on cursive scene text analysis. To create such a dataset, it may prove to be a starting point to address the intrinsic challenges associated with Arabic text in natural images.
2. Localization/detection of text: One of the difficult tasks in scene text images is to localize the text correctly by specific techniques or specialized tools. Extracting text with high precision is still a challenge for researchers to tackle. There exist various approaches to address this issue [4,10,30]. For the purpose of obtaining the segmentation of natural text accurately, Arabic text was localized manually in most of the reported research. A work about text localization of Latin script was presented with high precision by [2], but for cursive script, which is still an open research problem.
3. Text image preprocessing: The scene text image contains unwanted data, which ultimately need to be removed. Most reported work removed such noise manually, or it can be removed by image processing techniques. However, in practical applications when the dataset is large, it is recommended to define methods that may help to make clean text images, as explained in [74]. In that case, an automatic layout analysis tool is preferred, which may detect and remove unwanted information from the given text.
4. Implicit segmentation techniques: Arabic script is very complex in nature due to its various representations of the same characters and ligatures' combination. The association of diacritic marks to a base character is another very important issue. Whilst the automated tools cannot accurately segment this intrinsic script, most of the reported work relied on implicit segmentation of Arabic text. In this regard, LSTM provides an implicit segmentation approach for text recognition as reported in recent research on Urdu character recognition presented by [4, 5, 6]. If some research is performed on extensive segmentation approaches, which yields the solution as an implicit method, then it may provide new horizons for researchers who want to exploit their ideas in this direction by using Arabic text recognition in natural images.  
Apply state-of-the-art deep learning techniques: As mentioned before, most of the proposed Arabic scene text recognition has been exposed to an unsupervised learning algorithm. If applying the state-of-the-art supervised deep learning algorithm for the purpose of estimating correct parameters that are required for training the given sample, then this may lead to the proposal of a new dimension for learning complex patterns like Arabic, as tried by [30]. In this regard, one possible future direction may be the exposure of Arabic scene text images to supervised learning tasks and those deep learning classifiers that perform implicit segmentation of a given text image such as RNNs [7], Convolutional neural Networks (ConvNets) [13] and Bidirectional Long Short-Term Memory networks (BLSTM) [1].