This document presents the design of an intelligent e-book device that allows users to write and recognize characters. The primary objective is to create an electronic notebook, while the secondary goal is to help children learn English. It describes the basic block diagram, neural network structure, character scanning and recognition methods using artificial neural networks. The system will be built using a touch screen, LCD display and microcontroller on a single circuit board for portability. It is intended to be affordable, user-friendly and help replace paper documents with an electronic notetaking device.

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
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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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