# Arabic Cursive Text Recognition from Natural Scene Images
## 3 Advances of Deep Learning Network in Text Analysis
### Convolutional Neural Networks  
Although ConvNets are suitable for feature extraction, they can be used as a learning classifier. The convolutional aspect of learning the convolved features by the shared weight architecture make ConvNets a deep learning architecture. ConvNets comprise a three-layer architecture where the hidden layer consist of convolutional layers, which are usually divided into activation function, pooling layers, fully-connected layers and normalized layers. The convolutional layers take data from the input layer, convolve them and delegate them to the next layer. The convolutional features are further processed or selected by the pooling strategy, i.e., the maximum pooling that takes the maximum value from each receptive field of neurons and at the earlier layer. The average pooling and minimum pooling can also be applied to convolved features. The receptive fields are square boxes containing neurons, as represented in Figure 14. The detailed feature extraction procedure using ConvNets is very much applicable to complex data like Arabic whether presented as a printed, scene text or as handwritten form.  
The latest work on Latin or English using ConvNets was recently proposed by [61]. They proposed a multi-stage approach for script identification. At first, the text image was normalized by an x-height of 64 pixels while maintaining its aspect ratio, then applying the sliding window approach to extract \(32\times 32\) image patches, which they called stroke parts. Later, each part was presented to ConvNets to get the feature values. Furthermore, [PERSON] et al. [61] explained that each text line is represented as a stroke part's descriptor. They designed the same structure and followed the same pre-processing steps as explained in [62]. They employed the K-means algorithm on extracted patches to learn the convolutional kernels of ConvNets. Instead of extracting convolutional features from a single image with a single vector, as performed by [62], a set of convolutional feature vectors was extracted from parts of the image. Convolutional neural Networks (ConvNets) are another choice to learn the provided patterns, but they are instance learners, which is suitable where data are uncorrelated, like only the characters. If applying ConvNets on extracted image patches, this might not provide stable and good convolutional features because of the inherent complexity attached to cursive scripts. Although this is a good option for feature extraction and mostly applied for feature extraction, they further suggested the idea of merging the Naive Bayes Nearest Neighbour (NBNN) classifier with their proposed solution. The notion behind the said classifier is to compute the image directly without defining intermediate quantized descriptors. Thus, all extracted image patches were given to NBNN to compute the maximum difference of all provided templates that exist in other classes except for its own class. They reported the number of experimental variations on two publicly-available datasets and their own prepared dataset, named the Video Script Identification [8; 33] dataset, as well as their proposed dataset for multilingual script named MLe2e, which was comprised of 711 multilingual scene text images. Among various experiments as described in their manuscript, they also performed cross-validation of their trained network on the above-mentioned datasets. The trained network on their proposed dataset MLe2e obtained higher accuracy on the given test images of CVSI and ICDAR 2013, which was 70.22% and 94.70%, while on MLe2e, they obtained 91.67% accuracy.  
Figure 14: Deep ConvNets’ architecture.