\documentclass[a4paper]{article} \usepackage{amsmath} \usepackage{hyperref} \usepackage{graphicx} \usepackage{float} \title{Using local binary patterns to read license plates in photographs} % Paragraph indentation \setlength{\parindent}{0pt} \setlength{\parskip}{1ex plus 0.5ex minus 0.2ex} \begin{document} \maketitle \section*{Project members} Gijs van der Voort \\ Richard Torenvliet \\ Jayke Meijer \\ Tadde\"us Kroes\\ Fabi\"en Tesselaar \tableofcontents \pagebreak \setcounter{secnumdepth}{1} \section{Problem description} License plates are used for uniquely identifying motorized vehicles and are made to be read by humans from great distances and in all kinds of weather conditions. Reading license plates with a computer is much more difficult. Our dataset contains photographs of license plates from various angles and distances. This means that not only do we have to implement a method to read the actual characters, but given the location of the license plate and each individual character, we must make sure we transform each character to a standard form. Determining what character we are looking at will be done by using Local Binary Patterns. The main goal of our research is finding out how effective LBP's are in classifying characters on a license plate. In short our program must be able to do the following: \begin{enumerate} \item Extract characters using the location points in the xml file. \item Reduce noise where possible to ensure maximum readability. \item Transform a character to a normal form. \item Create a local binary pattern histogram vector. \item Recognize the character value of a vector using a classifier. \item Determine the performance of the classifier with a given test set. \end{enumerate} \section{Language of choice} The actual purpose of this project is to check if LBP is capable of recognizing license plate characters. Since the LBP algorithm is fairly simple to implement, it should have a good performance in comparison to other license plate recognition implementations if implemented in C. However, we decided to focus on functionality rather than speed. Therefore, we picked Python. We felt Python would not restrict us as much in assigning tasks to each member of the group. In addition, when using the correct modules to handle images, Python can be decent in speed. \section{Theory} Now we know what our program has to be capable of, we can start with the defining the problems we have and how we are planning to solve these. \subsection{Extracting a character and resizing it} We need to extract a character from a photo made of a car. We do not have to find where in this image the characters are, since this is provided in an XML file with our dataset. Once we have extracted the points from this XML file, we need to get this character from the image. For the nature of the Local Binary Pattern algorithm, we want a margin around the character. However, the points stored in the XML file are chosen in such a fashion, that the character would be cut out exactly. Therefore, we choose to take points that are slightly outside of the given points. When we have the points we want, we use a perspective transformation to get an exact image of the character. The final step is to resize this image in such a fashion, that the stroke of the character is more or less equal in each image. We do this by setting the height to a standard size, since each character has the same height on a license plate. We retain the height-width ratio, so we do not end up with characters that are different than other examples of the same character, because the image got stretched, which would of course be a bad thing for the classification. \subsection{Transformation} A simple perspective transformation will be sufficient to transform and resize the characters to a normalized format. The corner positions of characters in the dataset are provided together with the dataset. \subsection{Reducing noise} Small amounts of noise will probably be suppressed by usage of a Gaussian filter. A real problem occurs in very dirty license plates, where branches and dirt over a letter could radically change the local binary pattern. A question we can ask ourselves here, is whether we want to concentrate ourselves on these exceptional cases. By law, license plates have to be readable. However, the provided dataset showed that this does not mean they always are. We will have to see how the algorithm performs on these plates, however we have good hopes that our method will get a good score on dirty plates, as long as a big enough part of the license plate remains readable. \subsection{Local binary patterns} Once we have separate digits and characters, we intent to use Local Binary Patterns (Ojala, Pietikäinen \& Harwood, 1994) to determine what character or digit we are dealing with. Local Binary Patterns are a way to classify a texture based on the distribution of edge directions in the image. Since letters on a license plate consist mainly of straight lines and simple curves, LBP should be suited to identify these. \subsubsection{LBP Algorithm} The LBP algorithm that we implemented can use a variety of neighbourhoods, including the same square pattern that is introduced by Ojala et al (1994), and a circular form as presented by Wikipedia. \begin{enumerate} \item Determine the size of the square where the local patterns are being registered. For explanation purposes let the square be 3 x 3. \\ \item The grayscale value of the center pixel is used as threshold. Every value of the pixel around the center pixel is evaluated. If it's value is greater than the threshold it will be become a one, otherwise it will be a zero. \begin{figure}[H] \center \includegraphics[scale=0.5]{lbp.png} \caption{LBP 3 x 3 (Pietik\"ainen, Hadid, Zhao \& Ahonen (2011))} \end{figure} The pattern will be an 8-bit integer. This is accomplished by shifting the boolean value of each comparison one to seven places to the left. This results in the following mathematical expression: Let I($x_i, y_i$) be a grayscale Image and $g_n$ the value of the pixel $(x_i, y_i)$. Also let $s(g_i, g_c)$ (see below) with $g_c$ being the value of the center pixel and $g_i$ the grayscale value of the pixel to be evaluated. $$ s(g_i, g_c) = \left \{ \begin{array}{l l} 1 & \quad \text{if $g_i$ $\geq$ $g_c$}\\ 0 & \quad \text{if $g_i$ $<$ $g_c$}\\ \end{array} \right. $$ $$LBP_{n, g_c = (x_c, y_c)} = \sum\limits_{i=0}^{n-1} s(g_i, g_c) \cdot 2^i$$ The outcome of this operations will be a binary pattern. Note that the mathematical expression has the same effect as the bit shifting operation that we defined earlier. \item Given this pattern for each pixel, the next step is to divide the image into cells. \item Compute a histogram for each cell. \begin{figure}[H] \center \includegraphics[scale=0.7]{cells.png} \caption{Divide into cells (Pietik\"ainen et all (2011))} \end{figure} \item Consider every histogram a vector element and concatenate all histograms. The concatenation is the feature vector of the image. \item Feed these vectors to a support vector machine. The SVM will ``learn'' which vectors to associate with a character. \end{enumerate} To our knowledge, LBP has not yet been used in this manner before. Therefore, it will be the first thing to implement, to see if it lives up to the expectations. When the proof of concept is there, it can be used in a final, more efficient program. Later we will show that taking a histogram over the entire image (basically working with just one cell) gives us the best results. \subsection{Matching the database} Given the LBP of a character, a Support Vector Machine can be used to classify the character to a character in a learning set. The SVM uses the concatenation of the histograms of all cells in an image as a feature vector (in the case we check the entire image no concatenation has to be done of course). The SVM can be trained with a subset of the given dataset called the ``learning set''. Once trained, the entire classifier can be saved as a Pickle object\footnote{See \url{http://docs.python.org/library/pickle.html}} for later usage. In our case the support vector machine uses a radial gauss kernel function. The SVM finds a seperating hyperplane with minimum margins. \section{Implementation} In this section we will describe our implementation in more detail, explaining the choices we made in the process. We spent a lot of attention on structuring the code in such a fashion that it can easily be extended. \subsection{Character retrieval} In order to retrieve the characters from the entire image, we need to perform a perspective transformation. However, to do this, we need to know the coordinates of the four corners of each character. For our dataset, this is stored in XML files. So, the first step is to read these XML files. \paragraph*{XML reader} The XML reader will return a `license plate' object when given an XML file. The licence plate holds a list of, up to six, NormalizedImage characters and from which country the plate is from. The reader is currently assuming the XML file and image name are corresponding, since this was the case for the given dataset. This can easily be adjusted if required. To parse the XML file, the minidom module is used. So the XML file can be treated as a tree, where one can search for certain nodes. In each XML file it is possible that multiple versions exist, so the first thing the reader will do is retrieve the current and most up-to-date version of the plate. The reader will only get results from this version. Now we are only interested in the individual characters so we can skip the location of the entire license plate. Each character has a single character value, indicating what someone thought what the letter or digit was and four coordinates to create a bounding box. If less then four points have been set the character will not be saved. Else, to make things not to complicated, a Character class is used. It acts as an associative list, but it gives some extra freedom when using the data. When four points have been gathered the data from the actual image is being requested. For each corner a small margin is added (around 3 pixels) so that no features will be lost and minimum amounts of new features will be introduced by noise in the margin. In the next section you can read more about the perspective transformation that is being done. After the transformation the character can be saved: Converted to grayscale, but nothing further. This was used to create a learning set. If it does not need to be saved as an actual image it will be converted to a NormalizedImage. When these actions have been completed for each character the license plate is usable in the rest of the code. \paragraph*{Perspective transformation} Once we retrieved the corner points of the character, we feed those to a module that extracts the (warped) character from the original image, and creates a new image where the character is cut out, and is transformed to a rectangle. \subsection{Noise reduction} The image contains a lot of noise, both from camera errors due to dark noise etc., as from dirt on the license plate. In this case, noise therefore means any unwanted difference in color from the surrounding pixels. \paragraph*{Camera noise and small amounts of dirt} The dirt on the license plate can be of different sizes. We can reduce the smaller amounts of dirt in the same way as we reduce normal noise, by applying a Gaussian blur to the image. This is the next step in our program. The Gaussian filter we use comes from the \texttt{scipy.ndimage} module. We use this function instead of our own function because the standard functions are most likely more optimized then our own implementation, and speed is an important factor in this application. \paragraph*{Larger amounts of dirt} Larger amounts of dirt are not going to be resolved by using a Gaussian filter. We rely on one of the characteristics of the Local Binary Pattern, only looking at the difference between two pixels, to take care of these problems. \\ Because there will probably always be a difference between the characters and the dirt, and the fact that the characters are very black, the shape of the characters will still be conserved in the LBP, even if there is dirt surrounding the character. \subsection{Creating Local Binary Patterns and feature vector} Every pixel is a center pixel and it is also a value to evaluate but not at the same time. Every pixel is evaluated as shown in the explanation of the LBP algorithm. There are several neighbourhoods we can evaluate. We have tried the following neighbourhoods: \begin{figure}[H] \center \includegraphics[scale=0.5]{neighbourhoods.png} \caption{Tested neighbourhoods} \label{fig:tested-neighbourhoods} \end{figure} We call these neighbourhoods respectively (8,3)-, (8,5)- and (12,5)-neighbourhoods, after the number of points we use and the diameter of the `circle' on which these points lay. We chose these neighbourhoods to prevent having to use interpolation, which would add a computational step, thus making the code execute slower. In the next section we will describe what the best neighbourhood was. Take an example where the full square can be evaluated, so none of the neighbours are out of bounds. The first to be checked is the pixel in the left bottom corner in the square 3 x 3, with coordinate $(x - 1, y - 1)$ with $g_c$ as center pixel that has coordinates $(x, y)$. If the grayscale value of the neighbour in the left corner is greater than the grayscale value of the center pixel than return true. Bit-shift the first bit with 7. The outcome is now 1000000. The second neighbour will be bit-shifted with 6, and so on. Until we are at 0. The result is a binary pattern of the local point just evaluated. Now only the edge pixels are a problem, but a simple check if the location of the neighbour is still in the image can resolve this. We simply state that the pixel has a lower value then the center pixel if it is outside the image bounds. \paragraph*{Histogram and Feature Vector} After all the Local Binary Patterns are created for every pixel, this pattern is divided into cells. The feature vector is the vector of concatenated histograms. These histograms are created for cells. These cells are created by dividing the \textbf{pattern} in to cells and create a histogram of that. So multiple cells are related to one histogram. All the histograms are concatenated and fed to the SVM that will be discussed in the next section, Classification. We did however find out that the use of several cells was not increasing our performance, so we only have one histogram to feed to the SVM. \subsection{Classification} For the classification, we use a standard Python Support Vector Machine, \texttt{libsvm}. This is an often used SVM, and should allow us to simply feed data from the LBP and Feature Vector steps into the SVM and receive results. Usage of a SVM can be divided in two steps. First, the SVM has to be trained before it can be used to classify data. The training step takes a lot of time, but luckily \texttt{libsvm} offers us an opportunity to save a trained SVM. This means that the SVM only has to be created once, and can be saved for later usage. We have decided only to include a character in the system if the SVM can be trained with 70 examples. This is done automatically, by splitting the data set in a learning set and a test set, where the first 70 occurrences of a character are added to the learning set, and all the following are added to the test set. Therefore, if there are not enough examples, all available occurrences end up in the learning set, and non of these characters end up in the test set. Thus, they do not decrease our score. If such a character would be offered to the system (which it will not be in our own test program), the SVM will recognize it as good as possible because all occurrences are in the learning set. \subsection{Supporting Scripts} To be able to use the code efficiently, we wrote a number of scripts. This section describes the purpose and usage of each script. The dataset that is used is provided with the project, and should be located at \emph{images/characters}. \subsection*{\texttt{create\_characters.py}} Generates a file containing character objects with their feature vectors. Also, the learning set and test set files are created for the given combination of NEIGHBOURS and BLUR\_SCALE. \subsection*{\texttt{create\_classifier.py}} Generates a file containing a classifier object for the given combination of NEIGHBOURS and BLUR\_SCALE. The script uses functions from \texttt{create\_characters.py} to ensure that the required character files exist first. Therefore, \texttt{create\_characters.py} does not need to executed manually first. \subsection*{\texttt{find\_svm\_params.py}} Performs a grid-search to find the optimal value for \texttt{c} and \texttt{gamma}, for the given combination of NEIGHBOURS and BLUR\_SCALE. The optimal classifier is saved in \emph{data/classifier\_\{BLUR\_SCALE\}\_\{NEIGBOURS\}.dat}, and the accuracy scores are saved in \emph{results/result\_\{BLUR\_SCALE\}\_\{NEIGBOURS\}.txt}. Like \texttt{create\_classifier.py}, the script ensures that the required character object files exist first. \subsection*{\texttt{run\_classifier.py}} Runs the classifier that has been saved in \emph{data/classifier\_\{BLUR\_SCALE\}\_\{NEIGBOURS\}.dat}. If the classifier file does not exist yet, a C and GAMMA can be specified so that it is created. Therefore, it is not necessary to run \texttt{create\_classifier.py} first. \subsection*{\texttt{generate\_learning\_set.py}} Usage of this script could be minimal, since you only need to extract the letters carefully and successfully once. Then other scripts in this list can use the extracted images. Most likely the other scripts will use caching to speed up the system too. But in short, the script will create images of a single character based on a given dataset of license plate images and corresponding XML files. If the XML files give correct locations of the characters they can be extracted. The workhorse of this script is \texttt{plate = xml\_to\_LicensePlate(filename, save\_character=1)}. Where \texttt{save\_character} is an optional variable. If set it will save the image in the characters folder and pick the correct subdirectory based on the character value. So if the XML says a character is an 'A' it will be placed in the `A' folder. These folders will be created automatically if they do not exist yet. \section{Finding parameters} Now that we have a functioning system, we need to tune it to work properly for license plates. This means we need to find the parameters. Throughout the program we have a number of parameters for which no standard choice is available. These parameters are: \begin{tabular}{l|l} Parameter & Description \\ \hline $\sigma$ & The size of the Gaussian blur. \\ \emph{cell size} & The size of a cell for which a histogram of LBP's will be generated. \\ \emph{Neighbourhood}& The neighbourhood to use for creating the LBP. \\ $\gamma$ & Parameter for the Radial kernel used in the SVM. \\ $c$ & The soft margin of the SVM. Allows how much training errors are accepted. \\ \end{tabular} For each of these parameters, we will describe how we searched for a good value, and what value we decided on. \subsection{Parameter $\sigma$} The first parameter to decide on, is the $\sigma$ used in the Gaussian blur. To find this parameter, we tested a few values, by trying them and checking the results. It turned out that the best value was $\sigma = 1.9$. Theoretically, this can be explained as follows. The filter has width of $6 * \sigma = 6 * 1.9 = 11.4$ pixels. The width of a `stroke' in a character is after our resize operations around 10 pixels. This means, our filter in proportion to the smallest detail size we want to be able to see, so everything that is smaller is properly suppressed, yet it retains the details we do want to keep, being everything that is part of the character. \subsection{Parameter \emph{cell size}} The cell size of the Local Binary Patterns determines over what region a histogram is made. The trade-off here is that a bigger cell size makes the classification less affected by relative movement of a character compared to those in the learning set, since the important structure will be more likely to remain in the same cell. However, if the cell size is too big, the histogram loses information on locality of certain patterns. In order to find this parameter, we used a trial-and-error technique on a few cell sizes. During this testing, we discovered that a lot better score was reached when we take the histogram over the entire image, so with a single cell. Therefore, we decided to work without cells. A reason we can think of why using one cell works best is that the size of a single character on a license plate in the provided dataset is very small. That means that when dividing it into cells, these cells become simply too small to have a really representative histogram. Therefore, the concatenated histograms are then a list of only very small numbers, which are not significant enough to allow for reliable classification. We do lose information on locality of the patterns, but since the images are so small, this is not an issue. \subsection{Parameter \emph{Neighbourhood}} We tested the classifier with the patterns given in figure \ref{fig:tested-neighbourhoods}. We found that the best results were reached with the following neighbourhood, which we will call the (12,5)-neighbourhood, since it has 12 points in a area with a diameter of 5. \begin{figure}[H] \center \includegraphics[scale=0.5]{12-5neighbourhood.png} \caption{(12,5)-neighbourhood} \end{figure} \subsection{Parameters $\gamma$ \& $c$} The parameters $\gamma$ and $c$ are used for the SVM. $c$ is a standard parameter for each type of SVM, called the `soft margin'. This determines the amount of overlap that is allowed between two SVM-classes (which, in this case, are characters). Below, we will illustrate that the optimal value for $c$ is 32, which means that there is an overlap between classes. This can be explained by the fact that some characters are very similar to eachother. For instance, a `Z' is similar to a `7' and a `B' is similar to an `R'. $\gamma$ is a variable that determines the shape of the radial kernel, and as such determines how strongly the vector space of the SVM is transformed by the kernel function. To find the optimal combination of values for these variables, we have performed a so-called grid-search. A grid-search takes exponentially growing sequences for each parameter, and tests a classifier for each combination of values. The combination with the highest score is the optimal solution, which will be used in the final classifier. The results of our grid-search are displayed in the following table. The values in the table are rounded percentages, for better readability. \begin{tabular}{|r|r r r r r r r r r r|} \hline c $\gamma$ & $2^{-15}$ & $2^{-13}$ & $2^{-11}$ & $2^{-9}$ & $2^{-7}$ & $2^{-5}$ & $2^{-3}$ & $2^{-1}$ & $2^{1}$ & $2^{3}$\\ \hline $2^{-5}$ & 63 & 63 & 63 & 63 & 63 & 65 & 68 & 74 & 59 & 20\\ $2^{-3}$ & 63 & 63 & 63 & 63 & 63 & 65 & 70 & 80 & 60 & 20\\ $2^{-1}$ & 63 & 63 & 63 & 63 & 63 & 71 & 84 & 89 & 81 & 23\\ $2^{1}$ & 63 & 63 & 63 & 63 & 70 & 85 & 91 & 92 & 87 & 45\\ $2^{3}$ & 63 & 63 & 63 & 70 & 85 & 91 & 93 & 93 & 86 & 45\\ $2^{5}$ & 63 & 63 & 70 & 85 & 91 & 93 & 94 & 93 & 86 & 45\\ $2^{7}$ & 63 & 70 & 85 & 91 & 93 & 93 & 93 & 93 & 86 & 45\\ $2^{9}$ & 70 & 85 & 91 & 93 & 93 & 93 & 93 & 93 & 86 & 45\\ $2^{11}$ & 85 & 91 & 93 & 93 & 93 & 93 & 93 & 93 & 86 & 45\\ $2^{13}$ & 91 & 93 & 93 & 92 & 93 & 93 & 93 & 93 & 86 & 45\\ $2^{15}$ & 93 & 93 & 92 & 92 & 93 & 93 & 93 & 93 & 86 & 45\\ \hline \end{tabular} \\ The grid-search shows that the best values for these parameters are $c = 2^5 = 32$ and $\gamma = 2^{-3} = 0.125$. These values were found for a number of different blur sizes, so these are the best values for this neighbourhood and this problem. \section{Results} \subsection{Accuracy} The main goal of this project is to find out if LBP is a suitable algorithm to classify license plate characters. Of course, it is vital that the recognition of a license plate is correct, almost correct is not good enough here. Therefore, the highest possible score must be reached. According to Wikipedia \cite{wikiplate}, commercial license plate recognition that are currently on the market software score about $90\%$ to $94\%$, under optimal conditions and with modern equipment. Our program scores an average of $94.3\%$. However, this is for a single character. That means that a full license plate should theoretically get a score of $0.943^6 = 0.703$, so $70.3\%$. That is not particularly good compared to the commercial ones. However, our focus was on getting good scores per character. For us, $94.3\%$ is a very satisfying result. \subsubsection*{Faulty classified characters} As we do not have a $100\%$ score, it is interesting to see what characters are classified wrong. These characters are shown in appendix \ref{fcc}. Most of these errors are easily explained. For example, some `0's are classified as `D', some `1's are classified as `T' and some `F's are classified as `E'. Of course, these are not as interesting as some of the weird matches. For example, a `P' is classified as `7'. However, if we look more closely, the `P' is standing diagonally, possibly because the datapoints where not very exact in the XML file. This creates a large diagonal line in the image, which explains why this can be classified as a `7'. The same has happened with a `T', which is also marked as `7'. Other strange matches include a `Z' as a `9', but this character has a lot of noise surrounding it, which makes classification harder, and a `3' that is classified as `9', where the exact opposite is the case. This plate has no noise, due to which the background is a large area of equal color. This might cause the classification to focus more on the background than on the actual character. This happens for more characters, for instance a `5' as `P'. \subsection{Speed} Recognizing license plates is something that has to be done fast, since there can be a lot of cars passing a camera in a short time, especially on a highway. Therefore, we measured how well our program performed in terms of speed. We measure the time used to normalize a character, create its feature vector and classify it using a given classifier. The time needed to train the classifier needs not to be measured, because that can be done `offline'. We ran performance tests for the (8,3)- and (12,5)-patterns, with Gaussian blur scales of $1.0$ and $1.4$ respectively on the same set of characters. Because $1.5$ times an many pixel comparisons have to be executed (12 vs. 8), we suspected an increase of at least $0.5$ times the time for the first test to be the outcome of the second test. `At least', because the classification step will also be slower due to the increased size of the feature vectors ($\frac{2^{12}}{2^8} = 2^4 = 16$ times as slow). The tests resulted in $81ms$ and $137ms$ per character. $\frac{137}{81} = 1.7$, which agrees with our expectations. \\ Note: Both tests were executed using an AMD Phenom II X4 955 CPU processor, running at 3.2 GHz. \section{Discussion} There are a few points open for improvement. These are the following. \subsection{Training of the SVM} The current training of the SVM is simply done with a certain part of the entire dataset. An optimization in the training would be to use a number of different combinations of learning and test sets. This is called cross-validation. We think this would be a useful improvement since our learning set contains a number of weird characters. The effect of these characters would be decreased if we can use every character in the dataset to train the SVM. \subsection{Number of support vectors} It could be useful to check the number of support vectors used by the SVM. If this number is high in proportion to the number of dimensions in the feature vectors, it may be profitable to use a more simple kernel type for the SVM. \subsection{Other Local Binary Patterns} We had some good results but of course there are more things to explore. For instance we did a research on three different patterns. There are more patterns to try. For instance we only tried (8,3)-, (8,5)- and (12,5)-neighbourhoods. What might be done is to test which pattern gives the best result, for a wider range of neighbourhoods. We haven proven that the size and number of points do influence the performance of the classifier, so further research would be in place. The expectation is that using a larger diameter pattern, but with the same amount of points is worth trying. The theory behind that is that when using a Gaussian blur to reduce noise, the edges are blurred as well. By taking larger radius, you look over a larger distance, so the blurry part of the edge is skipped. By not using more points, there is no penalty in the time needed to calculate this larger pattern, so there is an accuracy advantage `for free'. \subsection{Context information} Unlike existing commercial license plate recognition software, our implementation makes no use of context information. For instance, Dutch early license plates consist of three blocks, one of two digits and two of two letters. More recent Dutch plates also consist of three blocks, two digits followed by three characters, followed by one or two digits. \\ This information could be used in an extension of our code to increase accuracy. \subsection{Potential speedup} One way of gaining time-wise performance is making a smart choice of local binary pattern. For instance, the (8,3)-neighbourhood has a good performance, but low accuracy. The (12,8)-neighbourhood yields a high accuracy, but has a relatively poor performance. As an in-between solution, the (8,5)-neighbourhood can be used. This has the same time-wise performance as (8,3), but a higher accuracy. The challenge is to find a combination of (number of points, neighbourhood size) that suits both accuracy and runtime demands. Another possibility to improve the performance speed-wise would be to separate the creation of the Gaussian kernel and the convolution. This way, the kernel will not have to be created for each feature vector. This seems to be a trivial optimization, but due to lack of time we have not been able to implement it. Using Python profiling, we learned that a significant percentage of the execution time is spent in the functions that create the LBP of a pixel. These functions currently call the \texttt{LocalBinaryPatternizer.is\_pixel\_darker} function for each comparison, which is expensive in terms of efficiency. The functions also call \texttt{inImage}, which (obviously) checks if a pixel is inside the image. This can be avoided by adding a border around the image with the width of half the neighbourhood size minus one (for example, $\frac{5 - 1}{2} = 2$ pixels in a 5x5 neighbourhood). When creating the feature vector, this border should not be iterated over. \section{Conclusion} It turns out that using Local Binary Patterns is a promising technique for license plate recognition. It seems to be relatively indifferent of the amount of dirt on license plates, which means that it is robust. \\ Also, different fonts are recognized quite well, which means that it is well suited for international use (at country borders, for example). Time-wise performance turns out to be better than one would expect from a large Python program. This gives high hopes for performance in any future implementation written in a C-like language. Given both of the statements above, we believe that with further experimentation and development, LBP's is absolutely a valid method to be used in license plate recognition. \section{Reflection} \subsection{Difficulties} During the implementation and testing of the program, we did encounter a number of difficulties. In this section we will state what these difficulties were and whether we were able to find a proper solution for them. \subsubsection*{Dataset} We have encountered a number of problems with the provided dataset. A number of these are to be expected in the real world, but they do make development harder. Others are more elementary problems. The first problem is that the dataset contains a lot of license plates which are problematic to read, due to excessive amounts of dirt on them. Of course, this is something you would encounter in the real situation, but it made it hard for us to see whether there was a coding error or just a bad example. Another problem is that there were license plates of several countries in the dataset. Each of these countries has it own font, which also makes it hard to identify these plates, unless there are a lot of these plates in the learning set. A problem that is more elemental is that some of the characters in the dataset are not properly classified. This is obviously very problematic, because it means that we had to manually verify the value of each character. As final note, we would like to state that a seemingly unrealistic amount of characters has a poor quality, with a lot of dirt, or crooked plates etc.. Our own experience is that the average license plate is less hard to read. The local binary pattern method has proven to work on this set, and as such has proven that it performs good in worst-case scenarios, but we would like to see how it performs on a dataset with a larger amount of readable, higher-resolution characters. \subsubsection*{libsvm} We also had trouble with the SVM for Python. The standard Python SVM, \texttt{libsvm}, had a poor documentation. There was no documentation whatsoever for a number of functions. This did not improve efficiency during the process of development. \subsection{Workload distribution} The first two weeks were very team based. Basically, the LBP algorithm day implemented in the first day, as result of a collective effort. Some additional `basic' functions and classes were created in similar fashion. This ensured that every team member was up-to-date and could start figuring out which part of the implementation was most suited to be done individually or in pairs. Gijs created the basic class structure and helped everyone by keeping track of what was required to be finished and who was working on what. Gijs also worked with Tadde\"us on the Local Binary Patterns code. Tadde\"us and Jayke worked on the SVM and Tadde\"us wrote all kinds of tests for testing what part went good and what went wrong, and what parameters had to be used. Fabi\"en created the functions to read and parse the given XML files with information about the license plates. Richard helped out whenever anyone needed a helping hand, and was always available when someone had doubts about what he was doing or needed to ask something. He also wrote an image cropper that cuts out a character with a given margin, which eventually turned out to be obsolete. The work on the report was mainly done by Jayke, assisted by Fabi\"en and Richard. The technical details were filled in by Gijs and Tadde\"us. \appendix \section{Faulty classified characters} \label{fcc} \begin{figure}[H] \hspace{-2cm} \includegraphics[scale=0.5]{faulty.png} \caption{Faulty classificatied characters} \end{figure} \begin{thebibliography}{9} \bibitem{lbp1} Matti Pietik\"ainen, Guoyin Zhao, Abdenour hadid, Timo Ahonen. \emph{Computational Imaging and Vision}. Springer-Verlag, London, 1st Edition, 2011. \bibitem{wikiplate} \emph{Automatic number-plate recognition}. (2011, December 17). \\ Wikipedia. Retrieved from \url{http://en.wikipedia.org/wiki/Automatic_number_plate_recognition} \end{thebibliography} \end{document}