# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Methods Designed for Feature Extraction
Later, they applied the pooling technique to summarize all the features representing the image. The evaluation was performed on two publicly-available datasets, i.e., Chars74 and ICDAR03, and reported 73.1% and 75.3% accuracy, respectively. They also proposed their own dataset named Arabic Scene Text Characters (STC) and evaluated the performance of their proposed system on it. Moreover, they reported 60.4% character recognition accuracy on their proposed dataset. Another paper that represents Chinese handwritten character recognition by considering SIFT descriptors was proposed by [58]. They modified the SIFT descriptor according to the characteristics of Chinese characters. The pre-processing was performed by passing each image through linear normalization and then performing elastic meshing [59] for the purpose of rectifying the invariance of the same characters written by various individuals. Moreover, they also extracted Gabor and gradient features of an image. Every extracted feature vector was compressed to 256 dimensions by Linear Discriminative Analysis (LDA). They performed experiments on different window sizes with various dimensions. The discussion about their detailed experiments can be found in their manuscript. Figure 6: Scale-invariant feature transformation of Arabic text. Figure 7: Character classification through SIFT features and sparse coding. A very interesting work on local features extraction based on template images was proposed by [60]. Their motive was to read the text in complex images in the presence of built-in noise associated with it. They proposed a new method for building the template in the absence of influential noise. After performing normalization, enhancement and binarization, they extracted scale-invariant features from the template image and from the complex image, as well. If some features were missing or not recognized, then their proposed geometrical verification algorithm was applied to correct the error. They evaluated their proposed technique on more than \(200,000\) images having three scripts, i.e., Chinese, Japanese and Korean script. On single character recognition, they obtained 94.1% accuracy, while on multiple character recognition, they obtained 89.6% accuracy. Hybrid features: In [48], the hybrid feature extraction approach was proposed by determining the stroke width, area, aspect ratio, perimeter, number and area of holes as a feature associated with each given text image. These features were examined and further passed to the classifier. The description about each feature is detailed in their manuscript. Their proposed method was evaluated on various benchmark datasets like Latin and multilingual scripts. The dataset, named as MSRA-TD500, ICDAR2011, ICDAR2013, except the ICDAR2011 dataset, and other datasets contain multilingual texts including Hindi and Arabic text, as well. Furthermore, they also evaluated the performance of the proposed algorithm on their collected data samples. [PERSON] et al. [14] used eight different hybrid features of the text detected by MSER. The identified features were character width, character surface, aspect ratio, stroke width, character height, character colour, vertical distance bottom line and MSER margin. They passed these features to the classifier to train the network. The two publicly-available datasets ICDAR2003 and Char74K dataset were evaluated. The reported accuracy was 74% on the Char74K dataset, while on ICDAR2003, they achieved 62% accuracy. Table 1 summarizes the detail about feature extraction approaches that have recently been proposed for cursive and non-cursive scripts. The IIIT-5k as mentioned in Table 1 is a dataset compiled by International Institute of Information Technology (IIIT) whereas, Indian Statistical Institute (ISI) compiled ISI-Bengali characters dataset. The ETL Character database was prepared by the Electrotechnical Laboratory (ETL) in Japan.