1. Character
Recognition using
Artificial Neural
Network
Submitted to : Dr. D.M. PATEL
Keval bavisi(19el019)
Harsh modi(19el023)

2. INDEX
• Introduction
• ANN
• Activation function
• Methodology
• Application
• Conclusion
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3. Introduction
-> A neural network is a
series of algorithms that
endeavours to recognize
underlying relationships in
a set of data through a
process that mimics the
way the human brain
operates.
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4. Artificial Neural Network
• ANN are programs designed to solve any problem by
trying to mimic the structure and the function of our
nervous system
• Neural network are based on simulated neurons,
which are joined together in a variety of ways to
form networks
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5. ANN(cont.)
Neural network resembles the human brain in the
following two ways:-
I. A neural network acquires knowledge through
learning
II. A neural network's knowledge is stored within the
interconnection strengths known as synaptic weight.
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6. ANN(cont.)
• ANN possess a large number of processing elements
called nodes/neurons which operate in parallel.
• Each link is associated with weights which contain
information about the input signal.
• Each neuron has an internal state of its own which is
a function of the inputs that neuron receives-
Activation level
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7. Activation function
• Bipolar binary and unipolar binary are called as hard
limiting activation functions used in discrete neuron
model.
• Unipolar continuous and bipolar continuous are
called soft limiting activation functions are called
sigmoidal characteristics.
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Activation functions Definition:-

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10. Methodology
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11. 1. Preprocessing
• Preprocessing stage has several tasks to be done:
1) Binarization
2) Noise Filtering
3) Smoothing
4) Normalization
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12. 2. Segmentation
The sequence/series of characters in an image is
divided into the individual set of characters. The
processed image is divided into line and character
segmentation.
(a) Line Segmentation
(b) Character Segmentation
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13. (a) Line Segmentation
i) Each text line may have a different skew angle
and
ii) part of the neighbouring text line may be
connected.
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14. (b) Character Segmentation
After separating each line, now we need to segment
each character. The character segmentation is a
major step, as the accuracy of characters is based on
how the characters are segmented
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15. 3. Feature Extraction
• Feature extraction based on character geometry
It extracts different line types that form a particular
character.
• This technique explained was tested using a Neural
Network which was trained with the feature vectors
obtained from the system proposed.
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16. 4. Classification and recognition
Design for ANN
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18. The procedure includes training model having 39
hidden layers and having 108 inputs and 26 outputs(a-
z).
Then followed by running main interface file and
extracting the featureout file and after running it
displaying it in notepad.
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No of layers 3 Node of layers
Input 108
Hidden 39
Output 26

19. Interface
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21. Application
• Character Recognition has a wide variety of
applications and there exists various ways to achieve
it
• Neural Network is a very effective method to
decipher any character of any language given the
right set of training. In cases such as preserving old
manuscripts where accuracy is more important, then
this method can be employed.
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22. • Also it is possible to improve the accuracy by adding
probability to each character. For example a capital
Q is very less likely to be found. Q is often mistaken
with O in most of the OCR software. The results then
can be forwarded for human review as nothing is full
proof.
• Many custom training set can be tweaked and saved
for specific purposes. These can be used on less
important documents and get accurate results.
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23. conclusion
The proposed neural network architecture has an
ability to classify the character patterns in some
degree. But it shows difficulties during the
classification of unknown samples. Since as a future
enhancement, it is expected to improve the current
architecture
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24. future works
• Make the system more font independent
• Increase no of nodes and layers in ANN
• try different recognition algorithms such
HMM(Hidden Markov Model)
• Improve the separation of touching characters.
• Improve efficiency of the feature extraction method.
• improve the system to identify any other char
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