character recognition using neural netwoks

7/31/2019 Character Recognition Using Neural Netwoks

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Kaushik Patel

Nirav Kothari Roshni Gehlot

Hetal Kachhi


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Artificial Intelligence is the science of making intelligent machines,especially intelligent computer programs, what is related to the processof understanding human intelligence with the help of computers, butArtificial Intelligence is not restricted to biologically observable methods.

In the course of 50 years of research, AI has developed a large number oftools to solve the most difficult problems in computer science. A few ofthe most general of these methods are discussed below: -

1. Search and Optimization.2. Logic.

3. Probabilistic methods for uncertain reasoning.4. Classifiers and statistical learning methods.5. Neural networks.6. Languages.


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Conversational Behavior

Data Mining

Driverless Cars

Robot Soccer


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Network of interconnecting artificial neurons.

Neurons are basically, decision-taking units.

Artificial neural networks may either be used to gain an understanding of

biological neural networks, or for solving artificial intelligence problems

without necessarily creating a model of a real biological system.

Computers are endowed with humanlike abilities to take decisions based

on current scenarios and circumstances.


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Feedforward Neural Network

Radial Basis Function Network

Kohonen Self-Organizing Network

Learning Vector Quantization

Recurrent Neural Network


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Operates in two modes, namely: -

1. Training &

2. Mapping

Comprises of Nodes or Neurons.

Every node is associated with its respective weight vector of the same

dimension as the input data vectors, and a position in the map space.

Forms a semantic map where similar samples are mapped close

together and dissimilar ones apart.

No Hidden Layers.


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It is simple and gets trained rapidly.

There are no hidden layers.

Output comprises of only one neuron. Thus only one winning neuron is

selected that will be closest to the input sample.


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1. Draw the Image and Enter corresponding character in text box: -

Use the mouse as an input to draw the image of characters. Enter the

corresponding character with which you want the drawn character to be

recognized in the text box.

2. Down sampling of Image: -

The images have to be cropped sharp to the border of the character in

order to standardize the images. The image standardization is done by

finding the maximum row and column with 1s and with the peak point,

increase and decrease the counter until meeting the white space, or the

line with all 0s. This technique is shown in figure below where a character

S is being cropped and resized.


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The image pre-processing is then

followed by the image resize again

to meet the network input

requirement, 5 by 7 matrices,

where the value of 1 will be

assigned to all pixel where all 10 by

10 box are filled with 1s, as shown:

Finally, the 5 by 7 matrices is

concatenated into a stream so

that it can be feed into network of

35 input neurons.


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3. Training the Neural Network: -

In training the neural network we actually make the neural network

learn how to recognize image. The process of training is adjusting the

individual weights between each of the individual neurons until we

reach close to the desired output.

4. Recognition: -

Draw the image to be recognized in drawing area and click on

recognize button. The best neuron will be fired as output and

recognition will be performed.

character recognition using neural netwoks

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