# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Classification Techniques for Scene Text Recognition
They evaluated their technique on ICDAR2003, ICDAR2005, the Stroke Width Transformation (SWT) dataset and their proposed Multilingual scale based region growing (MSRG) dataset. The SIFT keypoints were extracted and reported to have 81% accuracy on ICDAR, 87.90% on SWT and 86.46% on MSRG, respectively. The latest work on Latin or English using Convolutional neural Networks (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 they applied 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 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. If applying ConvNets on extracted image patches, it cannot provide stable and good convolutional features because of the inherent complexity attached to cursive scripts. Hence, they further suggested an idea of merging the Naive Bayes Nearest Neighbour (NBNN) classifier [66] 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 Video Script Identification Competition (CVSI-2015) [33], the ICDAR 2013 dataset and their proposed dataset named MultiLingual end to end (MLe2e), which is 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 given test images of CVSI and ICDAR 2013, which was 70.22% and 94.70%, while on MLe2e, they obtained 91.67% accuracy. Hybrid classifier: [PERSON]. [PERSON] et al. [54] proposed three classification schemes, i.e., the Nearest Neighbour (NN) classifier, SVM and Multiple Kernel Learning (MKL). They performed detailed experiments and reported results on their collected dataset. The collected samples contained Latin and Kannada text. There were 657 numbers of classes for Kannada text with few variations. The determination of accurate text is still a cumbersome task in text detection in natural image. Thus, they manually localized the text. Moreover, they gathered characters generated through synthetic means. The proposed technique was evaluated on 1922 natural images, 3410 handwritten English characters and 16425 Kannada characters written by 55 and 25 volunteers, respectively, as presented in Figure 8. They considered six different types of local features such as shape contexts [17], geometric blur [18], scale-invariant feature transform [67], spin image [68], maximum response of filters [69] and patch descriptor [70]. The detail about each feature can be seen in [54]. They also evaluated their proposed method by ABBY FineReader; furthermore, the reported results were obtained in the ICDAR Robust Reading Competition 2003 and 2004, as well The detailed experimentation was performed, and the obtained results on each of the features using three different classifiers on Latin and Kannada script were obtained as mentioned in Tables 1 and 2, while Figure 8 represents the Kannada text sample.