#3770 Σ0ρλδ!!

Most of the week was spent in gathering the images to train SVM, but still I was unable to gather the required number of images. It was quite an interesting week in terms of facing from angry owners to disgruntled guards. Finally I've decided to use the older XML file as it works pretty well with the given specifications. 

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.