## 2 Related Work  
Generally, connected components with binary thresholding, projection-based analysis, maximally stable extremal regions (MSER), sliding window and many CNN based methods have been extensively used for character extraction from license plates.  
The research conducted in [6; 7] claims to be implemented for multinational vehicle LPR but was only tested on Israeli and Bulgarian LPs. The two-stage character segmentation approach [8] is proposed for Chinese LPs by using CCA and bank of narrow-shaped filter (HSF). The CCA and kohonen neural network [9] is used to segment characters from Indian LPs. The CCA and Contour detection is used for character segmentation in Bangladesh and Iraq license plates [10; 11; 12]. CIE-LAB color space, OTSU segmentation and CCA are used to extract characters from a Pakistani vehicle license plate. This method [13] is suitable only for highly contrasting images and very sensitive to shadow. The projection-based methods [14; 15] have also been used for China and Iran respectively for LPCS. Usually, this technique is used with one of the binarization methods (OTSU, Adaptive, MET, etc.). The first image is converted into binary then horizontal and vertical projection is used to separate the characters. Maximally stable extremal regions (MSER) is also very famous method for LPCS as used in [16; 17] for Chinese license plates. Sliding window technique [18; 19] has also been used for America and Iran, but this method requires prior knowledge of window size and it is computationally complex. In recent years many CNN-based approaches are introduced to accomplish segmentation free LPR task as discussed in [20; 21]. Recurrent neural network and connectionist temporal classification is used in [20] while in [21], YOLO detector is used to accomplish this task and the target countries are China and Brazil respectively. The author proposed robust license plate recognition by introducing the concept of synthetic images to train a convolutional neural network [22]. A multi-task convolutional neural network [23] is proposed for license plate recognition with better accuracy and lower computational cost.  
Once the segmentation is done successfully, the next step is to recognize the characters separately. Generally, four approaches are used to accomplish the recognition task. The simplest and straightforward method is template matching [24; 25], which was used a lot in the beginning, but this method is not flexible enough for different fonts, noise, rotation and thickness changes. The artificial neural network [12; 26; 27; 28] is another very popular approach that has been used widely to recognize the license plate characters as well as many other classification tasks. The SVM classifier [13; 16; 29; 30] with various feature extraction methods is also used to perform recognition tasks as SVM is strong and fast classifier for real time applications. The extreme learning machines (ELMs) [31] and hidden Markov model (HMM) [32] are also used as license plate recognition classifiers. The concept of using the conventional low-level features is now considered old fashioned due to the revolution in computer vision by the introduction of deep learning methods. The deep convolutional neural network (CNN) architectures learn a hierarchy of discriminate features automatically that richly describe image content. In recent years, the use of deep learning frameworks [20; 21; 33; 34; 35] are also seen in ALPR system because of its powerful recognition capability.  
No method mentioned above (CCA, projection, MSER, sliding window) has the capability to handle multinational LPs directly. Some CNN based methods have capability to do this task, but they require a lot of training data which is not feasible in multinational scenario. Our proposed recognition framework is capable enough to recognize the license plate characters of different shapes, styles and having different background/foreground colors under various illumination conditions.