# Arabic Cursive Text Recognition from Natural Scene Images
## 3 Advances of Deep Learning Network in Text Analysis
### Recently-Proposed Deep Learning Research  
This section compiles the work to address the issues of scene text analysis presented in 2017 and 2018 using the deep learning architecture. Several novel deep learning works presented in recent years specifically proposed techniques for correct localization of cursive text. One such work was presented by [79]. The arbitrarily-oriented scene text detection was presented via rotation proposals. By using higher convolutional layers of the network, inclined rectangular proposals are generated with higher accuracy. The pooling strategy was proposed, which is adapted based on rotated Regions of Interest (RoIs). Their proposed technique was evaluated on three real-world text detection datasets, i.e., MSRA-TD500, ICDAR2013 and ICDAR 2015, and obtained good precision, recall and f-measure score. Another novel solution presenting the feature extraction method for scene text extraction was presented by [86]. The super-pixel approach was proposed based on the stroke feature transform approach that was based on deep learning feature classification for text detection. Their proposed technique used deep ConvNets, where each character was predicted by using the pixel value. The hand-crafted features were also used, and they proposed a solution by the fusion of both to obtain high performance system. They evaluated their proposed system on three benchmark datasets, i.e., the ICDAR2011, ICDAR2013 and SVT datasets, and reported the best precision, recall and f-measure score on these datasets. The work on text detection from video images was recently proposed by [80]. They proposed a Bayesian-based network for text detection and recognition. The proposed a system framework composed of three major components, i.e., text tracking, tracking-based text detection and tracking-based text recognition. The details about each category were presented in their article. The detection of text was improved by the proposed multi-frame integration. They evaluated the presented technique on their proposed video text (VidTEXT) dataset collected at University of Science and Technology Beijing (USTB), named USTB-VidTEXT which is publicly available. They reported encouraging results using their proposed techniques. An arbitrarily-oriented text detection by a fully-connected end-to-end convolutional neural network was presented by [81] and also discussed in [65]. The words were predicted by bounding boxes via a presented novel regression model. They evaluated their novel technique on four publicly-available datasets, i.e., the ICDAR 2015, ICDAR 2013, Component Objects in COntext (COCO)-Text images and SVT datasets. They reported state-of-the-art results on publicly-available datasets. The multiple convolutional neural network was proposed by [82]. Their proposed method consisted of three steps, i.e., text-aware, text extraction, text refinement and classification. The proposed architecture by traditional ConvNets, but using multiple layers was used. The proposed technique was evaluated on the ICDAR 2011, ICDAR 2013, ICDAR 2015 and SVT datasets. The details about their performed experiment can be found in their article. A work on cursive scene text feature extraction was proposed by [83]. The feature extraction approach for Chinese scene text was presented. The features were extracted from the complex structure of Chinese characters by ConvNets. A text structure component detector was presented as one of the layers in ConvNets, which produced robust results on Chinese scene character recognition. The presented technique was evaluated on two Chinese scene text datasets. By assessing the latest work on scene text analysis as summarized in Table 4, new techniques designed for text detection and classification are presented. Most of the work presented Latin scene text analysis, but few works presented cursive scene text analysis.