Intelligent ebook - make lives easier

1. Presented By
GROUP NO:04
NAME EXAM NO
KUMARVIVEK B8233072
AMOL MAHURKAR B8233078
MANOJ AUTADE B8233082
GUIDED BY
PROF. S.B.TAKALE
Intelligent e-Book
-making lives simpler
8/29/20131 Department of E&TC, Sinhgad College of Engineering

2. Objective
 Primary: Electronic – notebook
To design a device for jotting important data in various fonts
 Secondary: e-Handwriting practice book
To help children getting acquired to new languages such as
English
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3. Contents
 Origin of Idea
 PresentTechnologies
 Basic Block Diagram
 Structure of Neuron andANN
 Character Scanning
 Character Uniqueness
 Training of network
 Testing of network
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4. Origin of Idea
 Leave the bulky laptop at home
 Enjoy a digital life
 Free from paper piles
 Increase efficiency
 Document management
 Professional device for the meeting
 Cost Effective
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5. Present Technologies
 Character recognition usingANN based on OCR(Optical
Character Recognition)
 A compact, fast, and flexible implementation ofANNs is possible
with dedicated mixed-modeVLSI chips , which implements a two-
character recognizer using parallelogramVLSI architecture
 Recognize the cursive writing by extracting information about
knots using SPLINE function
 Integrated (offline + online) character recognition to improve the
recognition rate.
Figure: Nvidia Tegra 2
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6. Uniqueness of system
• Whole system in single microcontroller
• User specific
• Flexible
• Cost effective
• Compactness
• Work without PC interface
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7. Block Diagram
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8. Construction: Touch Screen
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9. Neural Network Basics: Neuron
 Part of Biological nervous
system
 Electrically Excitable.
 Contains synapses
Figure: Biological Neuron
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10. Artificial Neural Network
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11. Structure of ANN
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12. Work done on Character Recognition
Uniqueness Methods
1. Letter Density
2. Horizontal Scanning
3. Vertical and horizontal scanning.
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13. Character Scanning
Step1: Invoking RealTerm using MATLAB
Step2:Capturing co-ordinates and storing in .txt file
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14. Character Scanning
Step 3: Importing saved .txt file into MATLAB
Step 4: Differentiating between co-ordinates
Step 5: Plotting captured co-ordinates of character
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15. Character Uniqueness: Letter Density
Step 1: Dividing character into four quadrants
Step 2: Calculating Ratio variables from density of respective quadrants
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16. Character Uniqueness: Letter Density Results
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17. Character Uniqueness: Horizontal Scanning
Step 1: Creating 2-D image of scanned character
Step 2: Horizontal scanning
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18. Character Uniqueness: Horizontal Scanning Results
Step 3: Filtering unwanted data to horizontal density patches
Letter E LetterT
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19. Character Uniqueness: Horizontal & Vertical Scanning
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 Following five parameters are obtained by horizontal
scanning
 Following five parameters obtained by vertical scanning
42 7 32 7 36
50 21 27 14 12

20. Scanning Character
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FeatureVector (FV) = [20 22 35 23 21 27 24 23 25 28]

21. Training and validation
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 Input: 10 values obtained from horizontal and vertical
scanning.
 Hidden neurons: Decided such that it is minimum and
achieve desired performance.
 Output: Identity matrix of 26x26, each row for single
alphabet.
 Batch training:Altering the sequence of characters ten
batches are formed.
 Performance goal = 0.00000001.
 Maximum epochs = 5000.

22. Training and validation
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23. Result
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24. Design flow
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1) Training of network
2) Testing
i. Software
ii. Hardware

25. 8/29/2013Department of E&TC, Sinhgad College of Engineering27
X
Extract the
features from each
uppercase English
alphabet
Create the batch
containing mixed
characters
Decide targets for
each character
Set the
performance goal
Decide the
maximum number
of hidden neurons
, P
Initialize the
number of hidden
neurons , N=5
Train the network
Performance
achieved
N<P
N=N+1
STOP
Save the NET
Z
YESNO
NO
YES

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27. MATLAB Coder: Savior
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28. Applications
• Education (teachers, students)
• Healthcare (doctors, nurses)
• Insurance (sales representative)
• Legal (lawyers)
• Government ( staff)
• Businessmen (for daily meeting)
• Journalists
• Architects
• Receptionists
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29. Bill of material
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Component Cost (in Rs.)
ResistiveTouch Screen 300
Graphic Liquid Crystal Display 550
LPC2138 stack board 1100
Battery + Charger 400
PCB 200
Others 50
Total 2600
* All prices are indicative. In actual can vary by small amount

30. Hardware Setup
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31. Hardware Setup
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32. Hardware Setup
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33. Hardware Setup
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34. References
 S N Sivanandam, S Sumathi, S N Deepa,“Introduction to Neural Networks
using MATLAB 6.0” ,Tata McGrawHill, 2nd Editon
 Angkoon Phinyomark,“A CAD approach based onArtificial Neural Network”,
Journal of IET,Volume 92, July 18
 L. D. Jackel,“A Neural Network Approach to Handprint Character
Recognition”
 Sameh E. Rehan,“VLSI Implementation of a ModularANN Chip for Character
Recognition”
 KarinaTOSCANO, “Cursive Character Recognition System”
 [HiroshiTanaka,“Hybrid Pen-Input Character Recognition System Based on
 Integration of Online-Offline Recognition”
 http://www.learnartificialneuralnetworks.com/backpropagation.html
 http://www.learnartificialneuralnetworks.com/#Intro
 http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
#Human andArtificial Neurones - investigating the similarities
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35. Thank you
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