final ppt
Transcript of final ppt
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AIR BORNE CHARACTER RECOGNITION SYSTEM
Project Guide
Mrs.Sundari Tribhuvanam
Project Guide Mrs.Sundari Tribhuvanam
Nithin Chandra Bharadwaj N
1AT08EC066
Naveen Kumar S 1AT08EC069
Praveen G 1AT08EC075
Srikanth N S 1AT08EC100
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Abstract Character recognition system is a new concept which has
been introduced lately due to the growing demand for security
Digital imaging allows the operator to post-process the image that allows the operator to manipulate the pixel shades
Just by the movement of the hand, the characters written in plain air are identified
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Introduction Computers still receive input from traditional low
bandwidth devices such as a keyboard or a mouse
These devices are inconvenient for providing high degrees-of-freedom inputs
Growing interest on Human-Computer Interaction (HCI) to develop a machine that can understand audio and visual based information
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Flow Chart
Start
Initialization of Camera
Writing the character
A
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A
Acquiring Frames
Frames Acquired
=42
No
A
Add Frames
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A
Noise Elimination
Character Mapping
Display Character
Stop
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Requirements
Dark Room for character capture
Black Screen
Hardware tools
12 MP iBall Web Camera
650nm hand-held LASER( Helium-Neon LASER admissible)
Software tools
Windows 7/Vista 32/64 operating system
Matlab 2009/2011 editions with Image Processing tool box
Webcam software to configure the device outside Matlab(only if necessary)
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Image Acquisition
Properties of camera:
Frame Rate = 15 fps
Frames Per Trigger = 40
Backlight Compensation = off
Color Space = RGB
Compression=None
Capture video from camera
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Convert video to frames
Obtain the number of frames
Write the frames into a structural array
S=struct('field1',values1,'field2',values2,...)
Creates a Structure array with the specified fields and values
Combine frames to form a single image
Concatenating strings
Combinedstr = strcat(s1, s2, ..., sn) horizontally concatenates strings in arrays s1, s2, ..., sn
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Image Acquisition(Bright Background)
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Noise Reduction Histogram Processing
Histogram of a digital image with intensity levels in the range [0,L-1] is a discrete function h(rk)=nk ,where rk is the kth intensity value and nk is the number of pixels in the image with the intensity rk.
Median Filter
The median, x, of a set of values is such that half the values in the set are less than or equal to x and half are greater than or equal to x.
The median represents the 50th percentile of a ranked set of numbers.
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Noise Reduction
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Line width increased using 3*3 window
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Image Mapping
Read the Noise reduced image and threshold the size of the image
D = SIZE(X), for m-by-n matrix X, returns the two-element row vector D = [M,N] containing the number of rows and columns in the matrix
Convert the RGB image into a GRAY scale image
Elimination of residue noise using a 2-D median filter
B = MEDFILT2(A,[M N]) performs median filtering of the matrix A in two dimensions. Each output pixel contains the median value in the M-by-N neighborhood around the corresponding pixel in the input image
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Calculate connected components
Label connected components in 2-D binary image
L = BWLABEL(BW,N) returns a matrix L, of the same size as BW containing labels for the connected components in BW
Resize the image to 42*24 pixels for character identification
Read the letter from the image and store the letter in a text document
Display the letter in the text document
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Sobel operator:
z1 z2 z3
z4 z5 z6
z7 z8 z9
Gx = (z1+2z2+z3)-(z7+2z8+z9)Gy =(z1+2z4+z7)-(z3+2z6+z9)
Sobel masks
-1 -2 -1
0 0 0
1 2 1
-1 0 1
-2 0 2
-1 0 1
0 1 2
-1 0 1
-2 -1 0
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BW = EDGE(I,'sobel') specifies the Sobel method.
BW = EDGE(I,'sobel',THRESH) specifies the sensitivity threshold for the Sobel method. EDGE ignores all edges that are not stronger than THRESH. If you do not specify THRESH, or if THRESH is empty ([]), EDGE chooses the value automatically.
BWLABEL Label connected components in 2-D binary image. L = BWLABEL(BW,N) returns a matrix L, of the same size as BW, containing labels for the connected components in BW. N can have a value of either 4 or 8, where 4 specifies 4-connected objects and 8 specifies 8-connected objects; if the argument is omitted, it defaults to 8.
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CALCULATING THE CONNECTED COMPONENTS
BW = logical([1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0 0 0]); L = bwlabel(BW,4) [r,c] = find(L == 2)
L =
1 1 1 0 0 0 0 0 1 1 1 0 2 2 0 0 1 1 1 0 2 2 0 0 1 1 1 0 0 0 3 0 1 1 1 0 0 0 3 0 1 1 1 0 0 0 3 0 1 1 1 0 0 3 3 0 1 1 1 0 0 0 0 0
r =
2 3 2 3
c =
5 5 6 6
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text1.txt
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Conclusion
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Future Developments Identifying words with atleast two characters
Interfacing the final character on to a DSP processor and observing the character on an LCD Display