# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Arabic Scene Text Datasets  
The dataset plays a very dominant role in investigating the potential of numerous classifiers. Various efforts have been reported for capturing and preparing the datasets for Arabic text in natural images in the recent past. Some articles presented a survey on available open access datasets and tools specifically designed for Arabic text detection and recognition in video frames captured by news channels [76]. The benchmark dataset for Arabic scene text still requires more effort to standardized the research as far as Arabic scene text analysis is concerned. The description of a few available datasets is revealed as follows. In addition to available datasets, this survey also describes the details of our collected data samples. ARASTEC (ARAbic Scene TExt Characters) 2015: A recently-compiled dataset was prepared by [31]. ARASTEC is an abbreviation of ARAbic Scene TExt Characters. They captured 260 natural images containing Arabic text. Images were taken from sign boards, hoardings, and advertisements. They manually segmented characters from images and obtained 100 classes depending on the position of a character in a word depicting 28 Arabic characters. They obtained 30-40 variations of each class. The sample Arabic text image of the ARASTEC2015 dataset is shown in Figure 9.  
ALIF: This is considered one of the first Arabic embedded text recognition datasets, proposed by [77]. The gathered samples were taken from various Arabic TV broadcasts, e.g., Al Jazeera, Al Arabiya, France 24 Arabic, BBC Arabic and Al-Hiwar Arabic. They localized Arabic text from 64 recorded videos. There is a wide variety of text specification like colours, styles and font size. In addition, the text visibility is also impacted by acquisition conditions, e.g., contrast, luminosity and background colour, which make the dataset challenging to apply to evaluate state-of-the-art techniques. The ALIF dataset contains 89,819 characters, 52,410 paws and 18,041 words. The data that were collected had more than 20 fonts. Their proposed network was trained on 4152 text images, which covered a wide variability of acquired text. The potential of the trained network was evaluated on three different test sets. The first test included 900 images, which were selected from the same channels used during training. The second test set was applied in the same setting as the previous one, but with 400 additional images. The third test set had large variations of text with respect to font and size. It had 1022 text images in total. The complexity associated with camera-captured text images is different in comparison with printed text, as shown in Figure 10.  
APTI (Arabic Printed Text Image): The APTI database was introduced by [78]. They generated Arabic data synthetically with font variations, different sizes and different styles like bold/italic. The samples were also decomposed into ligatures. The lexicon was divided into 113,284 words written in 10 different Arabic font styles by fixing 10 font sizes, but in four different font styles. Their dataset had 45,313,600 single-word images with more than 250 million characters. Figure 10: Embedded Arabic text of the ALIF dataset. Figure 9: Sample image from the ARASTEC 2015 dataset. In camera-captured text images, there is a need to deal with non-text captured objects, which are categorized as noise in an image that should be removed as a part of the pre-processing. Another challenging factor attached to camera-captured text images is illumination variation. The details from the given facts suggest that extensive pre-processing is required for camera-captured text image. As the APTI dataset was generated synthetically, this dataset can be treated as another category, because it was generated differently, as shown in Figure 11.  
English-Arabic Scene Text Recognition (EASTR) dataset: The unavailability of an Arabic scene text dataset is a main hurdle in performing detailed research in this particular script as far as Arabic text recognition in natural images is concerned.