# Towards Automatic License Plate Recognition in Challenging Conditions
## 2 Related Work
The RPN is trained end-to-end to generate high-quality region proposals that are later used by Fast R-CNN for detection. In this work, the RPN and Fast R-CNN are also merged into a single network by sharing their convolutional features. For the very deep VGG-16 model, this system has a frame rate of 5 fps on a GPU, while achieving encouraging object detection accuracy on several datasets with only 300 proposals per image. In [31], a wavelet transform based technique to extract license plates from cluttered images is developed. This method comprises of three major stages, which are (i) extracting important contrast features through wavelets. Then, finding a reference line in HL subimage plays an important role to locate the desired license plate region roughly. According, (ii) decrease the searching region of license plate to speed up the execution time, and (iii) localization of license plate through manual adjustments. More importantly, the proposed detection method can locate multiple plates with different orientations in one image. Since the feature extracted is robust to complex backgrounds, the proposed method works well in extracting differently illuminated and oriented license plates. The average accuracy of detection is 92.4%. In [32], authors made use of a combination of the MSER and the stroke width transform (SWT) to detect and isolate the LP character regions. The license plates were finally bordered using the probabilistic Hough transform. The authors discuss that character-based methods are reliable and can lead to a high recall. However, the other text in the image background has a significant impact on performance. This method requires multiple cameras before the system is placed for evaluation. In [33], an interesting license plate recognition system is developed using a sequence of deep CNNs. These CNNs are trained and fine-tuned so that they are robust under different conditions (for instance, lighting, occlusion, or tilt) and work across a variety of license plate templates that include different sizes, backgrounds, or fonts. In [34], a novel line density filter approach was developed that connects regions with high edge density and removes sparse regions in each row and column from a binary edge image. This study indicates that edge-based methods are fast in computation but cannot be applied to complex images because they are too sensitive to unwanted edges. In [35], the developed LP method consists of three modules for plate detection, character segmentation, and recognition. This method also formulates edge clustering to solve plate detection for the first time. A bilayer classifier, which is improved with an additional null class, is empirically proven to be better than previous methods for character recognition. However, this method is evaluated only on a single dataset, which was also gathered by the authors themselves. In [36], license plate detection and recognition are tackled in standard natural scene images via the development of a segmentation-free method. Inspired by the success of DNNs, these are deployed to learn high-level features in a cascade framework, which leads to improved performance on both detection and recognition. This work also trains 37 CNNs to detect all characters in an image, which results in a high recall. Later, to improve the IoU ratio, bounding box refinement is carried out based on the edge information of the LPs. This method extracts license plates effectively with both high recall and precision. Last, a recurrent neural network with long short-term memory (LSTM) is trained to recognize the sequential features extracted from the whole license plate via CNNs. For scene and lighting variations, this method needs to be further explored. In [37], a unified deep neural network is proposed that localizes license plates and recognizes the letters simultaneously in a single forward pass. This whole network is trained end-to-end and achieves the LP recognition task in a single network, avoiding intermediate error accumulation and resulting in faster processing speed.