#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.