#3770 Σ0ρλδ!!

The major challenge in training ANN is the large dataset required to get more accurate results. More and more unique images are required  for each character. Also all of these images should be normalized and have same size,i.e., 10 x 10 pixels. To get this dataset we have to use the segmented characters produced by SVM - the Ouroboro problem. No clear solution to this problem has been found. 

Today, training data requirements for ANN were identified. It will require more than 20 images for each character. In our case, we will need to gather data for  set of 26 characters (10 numbers + 26 alphabets).

Its working principle is mathematically heavy. Basically, it uses some input values, processes it using a function and then matches the value to a particular class.  

Today, the role of ANN in our system was analysed. The main role of ANN in our system was understood which is to recognise different characters present on the number plate. 

Artificial Neural Network an it s major aspects were understood today. In computer science and related fields, artificial neural networks are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network. For example, in a neural network for handwriting recognition, a set of input neurons may be activated by the pixels of an input image representing a letter or digit. The activations of these neurons are then passed on, weighted and transformed by some function determined by the network's designer, to other neurons, etc., until finally an output neuron is activated that determines which character was read. In layman terms, it is used to identify the unique signature between different classes which falls  under same category.

Today, challenges to to train such a large SVM classifier were identified. A lot of training data is required to train SVM. Under given timeline, such a large dataset may not be collected in time. This is the major challenge in training SVM. 

Today, training data requirements were identified to train SVM. It requires 75 plate and 35 non-plate images. If these requirements are not, earlier data stored in training file (XML) will be used. 

The mathematical aspects and its working process was better understood. As well as how to implement  it in the code was also researched. 

Today, we got to understand what function it will perform in our proposed system. Support vector machine (SVM) algorithm will be used to classify number plate and non-number plate images. 

In  machine learning ,  support vector machines  ( SVMs , also  support vector networks ) are  supervised learning  models with associated learning  algorithms  that analyze data and recognize patterns, used for  classification  and  regression analysis .  Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier.  An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. In layman terms, SVM is used to differentiate between two different classes of training data.  

For plate localization and character recognition, pattern recognition algorithms are used to teach machine to identify different patterns. This can be achieved using OpenCV's Statistical Machine Learning Library. Today, we have learnt about them and selected two such pattern recognition algorithms that will be used in this project, i.e., SVM and ANN. These algorithms will be discussed in next two weeks.

The most critical component of the ANPR system is its pattern recognition system. In pattern recognition system, the machine is taught to recognize patterns. It will be used to locate number plate in images and for character recognition. In order to do this, the system needs to be trained using OpenCV's Statistical Machine Library. The main algorithms used to realize this are Support Vector Machine algorithm and Artificial Neural Network algorithm.  To get a better understanding of OpenCV library and its machine learning library, online sources will be sorted and the most suitable one will be used. More about this will be discussed in next posts. 

With the viral spread of digital technology in today's world, it has also reached the shores of our country. The spark of digital revolution has been lighted by the advent of powerful and affordable computing devices, as well as internet. This has also created a ocean of videos and images everywhere. For anyone who wishes to develop simple or sophisticated imaging applications, OpenCV library is the tool to use. OpenCV (Open Source Computer Vision) is an open source library containing more than 500 optimized algorithms for image and video analysis. Since its introduction in 1999, it has been largely adopted as the primary development tool by the community of researchers and developers in computer vision. OpenCV was originally developed at Intel by a team led by Gary Bradski as an initiative to advance research in vision and promote the development of rich, vision-based CPU-intensive applications. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iO

Today previous research papers published  on developing an ANPR system specific to Indian conditions were studied. Most of these papers were found using Google. Most of them give a brief description about mathematical aspects and different methodologies to realize such a system. But there is no code implementation part which can be studied to understand or develop such a system. By developing a opensource online repository for such projects, such project can be further developed and improved upon until it can be implemented in more real life situations. It could be a evolutionary model in which additional functions can be added. These papers are: Prathamesh Kulkarni (Student Member, IEEE), Ashish Khatri, Prateek Banga, Kushal Shah: A Feature Based Approach for Localization of Indian Number Plates, Dept. of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering, University of Pune, India. Shishir Kumar, Shashank Agarwal, Kumar Saurabh: License Plate Reco

Today, the existing systems were to be studied. But it was found that the existing system for number plate recognition is still basically in pre-technological era. Most of the recognition work is done manually because of which the whole process is long, tedious and lacks any transparency. It is unable to take benefits of the present and future technological capabilities. Due to which most of the traffic and security applications are done through outdated or pedestrian methods which are slow, error-prone and impractical in current scenario. With the advent of information technologies, it has become an increasing necessity that all existing systems be upgraded and integrated with this new technology in order to make these systems more advanced, transparent, fast and better than before. Another motivation for this project was the heinous crime that was committed on 16 December, 2012 in Delhi. In which the 23 year old woman was brutally gang-raped in a moving bus. That bus did not h

Today, analysis of user requirements was done taking under consideration the constraints and research work done. The User Requirement Analysis suggest that the main priority is to develop a simple and cost-effective system that is able to recognise characters from a given number plate image. Taking this into consideration following specifications are made: Instead of IR cameras, normal 5MP cameras will be used. Number Plates of Private Vehicles like cars that are medium-sized and follow standard number plate format will be considered. No special cases like VIP number plates and temporary number plates will be considered.  To train the system, non-indian number plate images can be used to meet the desired data set requirements. 

Today, the constraints were identified under which the project needs to be realized. The main constraints are given below:  Lack of strict enforcement of standardized number plate guidelines : This causes issues in training the system and results in less accurate results. Lack of a centralized traffic database system : This causes issues in developing real time applications and data gathering to produce more accurate results. Small Dataset : To develop a real life application, it is necessary to train and develop the system with a large dataset in order to minimize errors and have a better accuracy rate. But due to given time period gathering such large dataset is not possible. 

As seen in the previous post, the format and specifications of the number plate changes depending on the type of vehicles.Therefore, it is necessary to select the correct class of vehicle as per user requirements and constraints. This is also necessary because size and location of number plate is also critical to the system. The types of vehicles that will be considered are given below: Private  Commercial Government Four-wheeler Two-wheeler Heavy Vehicle Small Vehicle                                                                                                                                   

In India, there are different types of number plates in existence that differ in background color, size and even format. Different formats are defined for military vehicles, diplomatic vehicles, government vehicles and temporary license plates. Plate specifications like font, background color, font size and plate size is also defined on basis of different classes of vehicles. A tabular information regarding this is given below:                      This wide range in specifications can be a problem in designing a proper methodology for the project.  Therefore, on the basis of user requirements analysis and given time-frame, a standard specification will be selected.

The main purpose of ANPR project is to recognize the characters present in the number plate. To do this first, understanding the standard format of Indian Number Plate is required.  Since June 1, 2005, the Government of India has introduced High Security  Registration (HSR) number plates which are tamper proof. All new motorized  road vehicles that come into the market have to adhere to the new plates, while  existing vehicles have been given two years to comply with. Features  incorporated include the number plate having a patented chromium hologram; a  laser numbering containing the alpha-numeric identification of both the testing  agency and manufacturers and a retro-reflective film bearing a verification  inscription "India" at a 45-degree inclination. The numbers would be embossed  on the plate, rather than being painted for better visibility. The term "India" is to  be in a light shade of blue.  But still most of the vehicles don't follow this format.