# 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  
Classification refers to the statistical analysis of training observations under supervised and unsupervised learning. The classifiers analyse the numerical properties of a given image. These numerical properties are the distinctive features that represent the image in question. There are numerous state-of-the-art classifiers that have been proposed during recent years, but most of them depict the process of learning through unsupervised methods [15; 62]. The summarized version of a few proposed classification techniques with their references are elaborated as follows. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Feature Extraction Approach** & **Script** & **Dataset Used** \\ \hline [PERSON] et al. [10] & Histogram of Oriented Gradients (HOG) & Latin & Char74k, ICDAR03-CH \\ [PERSON] et al. [14] & Stroke orientation & Latin & ICDAR2011 \\ [PERSON] et al. [31] & SIFT & Arabic, Latin & ARASTEC \\ [PERSON] et al. [35] & Global and local HOG & Latin & Char74k, ICDAR2003 \\ [PERSON] et al. [42] & HOG & Chinese and Bengali & IIIT-5k word, Pan Chinese, ISI Bengali Characters \\ [PERSON] et al. [59] & Minimum Euclidean distance, SIFT & Chinese & ETL9B \\ [PERSON] et al. [60] & SIFT & Chinese, Japanese, Korean & Datasets A, B, C, D, E \\ [PERSON] et al. [54] & Geometric blur, shape context, SIFT, & & (own compiled) \\  & Patches, spin images, Maximum Response (MR8) & Latin, Kannada & Own compiled \\ [PERSON] et al. [61] & Convolutional neural network and K-means & Multilingual & CVSI, MLe2e dataset \\ [PERSON] et al. [11] & SIFT & Latin, multilingual & ICDAR, SWG, MSRG \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature extraction approaches of cursive and non-cursive scene text. Nearest neighbour classifier: The nearest neighbour classifier is categorized as a non-parametric method used to train the given sample based on the closet trained neighbour in the feature vector. An interesting work representing nearest neighbour as the feature vector was proposed by [14]. The candidate regions that have been extracted by oriented stroke information are labelled by Unicode. In their experiments, they took the extracted region as black in colour with a white background. They considered 62 character classes written in 90 font styles. In total, they had 5580 training samples. In another manuscript, the nearest neighbour classifier was used by [63] to train and classify the given pattern. For the purpose of obtaining the maximum performance, they set the value of \(K\) to 11. They evaluated their proposed technique on the ICDAR2011 Robust Reading Competition dataset [2] by using an evaluation protocol of ICDAR2011 [64]. The results were reported as recall of 66.4%, while the precision was measured as 79.3%. They concluded that their recall results were better than the winner of the ICDAR2011 Robust Reading Competition, which reported 62%, and [PERSON]'s method, which reported 63% [65]. Neural network as a classifier: The artificial neural network is considered as a reliable classifier, which is inspired by the human way of learning things. A recent and novel work on isolated Arabic scene character recognition using a deep learning classifier was proposed by [13]. They used a convolutional neural network and trained the classifier on different character orientations. They further formulated the training on \(3\times 3\) and \(5\times 5\) filter sizes by keeping the stride values of one and two. Moreover, various learning rates have been applied for the purpose to get maximum accuracy. They reported a 0.15% error rate on the recognition of isolated characters. The work presented by [PERSON] et al. [11] is scale-based region growing technique to detect text in an image, while the neural network was proposed to learn the pattern. They decomposed the input into 128 dimensions, which is further passed on to a hidden layer network of 40 in size. They trained 67% of the dataset samples, while the remaining 33% were used to test the trained network.