## 4 Simplified Flow for Mobile Platform
### Flow of Character Detection and Localization  
The next part of the ALPR system is detection and localization of the characters in the cropped image of the detected plate. The character classification network is also a fully convolutional neural network, comprised of 12 convolutional layers. Similar to the plate classifier, it simultaneously performs the detection and localization of characters. The character detector provides 33 output values per classification. These values represent the probability for each of the 32 alpha-numeric characters valid for Italian license plates or no character present (the 33 rd output). The output of the character localizer comprises four values (the top left and the bottom right coordinates (x,y)) that represent the rectangle that encloses the detected character. Although this localization is not required for a correct classification of the license string, it provides a graphical output specifying the location of each character.  
First of all, the crop from plate classifier is resized (188 \(\times\) 40) to provide appropriate character classifications (note that this crop is of higher resolution than the input image for plate classification, in which the resolution is not necessarily required to be good enough to read the characters). The network has three max-pooling layers, the stride between classifications are eight pixels, and by rescaling the crop to a height of 40 pixels the classifications are reduced to a single line, significantly  
Figure 6: Rescaled input image.  
reducing the complexity. Maintaining the aspect ratio, this yields a crop of 188 \(\times\) 40 pixels, which, given the stride length of eight, yields 21 partly overlapping classifications (as shown in Figure 7).  
A single classification (slot) represents a 24 \(\times\) 40 portion of the image which may contain a character. The detected character in each classification (slot) is decided by the highest value of the detector output (out of 33 values). These 33 values represent the confidence ratings for each of the digits (0 to 9), Italian letters (22), and a background (null) value. These outputs are not softmaxed, as these values can be directly used for classification.  
A generic Italian license plate contains seven characters (three digits and four alphabetic characters). This simplified ALPR system must select seven characters from the 21 outputs (classifications) generated by the network. To generate the final result, a trivial heuristic approach is adopted in the proposed flow. This heuristic method divides the 21 classifications (slots) into seven small clusters composed of three slots each. Note that the slots are overlapping, and depending on the accuracy of the plate localization, the same slot is not always the most centered. The classification corresponding to the highest confidence rating within each cluster is selected for the final result, as shown in Figure 7.  
Furthermore, the size of the cropped image influences the result given by the character detector. By choosing an offset on the plate localization defining the character input crop, the accuracy and the number of classifications detected by the character classifier can be varied. Figure 8 shows that the cropped plate with an offset of four pixels provides exactly seven classifications (characters) of an Italian license plate.