As computer, compress technology, storage media and high speed communication skill are developed dramatically; digital video has become one of the most important elements in many applications such as education, news and games.

1. EXTRACTION OF TEXT FROM VIDEO
USING MATLAB
Under the esteemed supervision of
M.Naresh Babu,M.Tech;
Assistant Professor
Department of ECE
SREE VIDYANIKETHAN ENGINEERING COLLEGE
(AUTONOMOUS)
Sri Sainathnagar, A.Rangampet, Tirupathi-517102
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2. CONTENTS:
 Introduction
 Text Extraction
 Process of Extraction
 Algorithm
 Flowchart
 Advantages
 Disadvantages
 Applications
 Conclusion
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3. INTRODUCTION
 As computer, compress technology, storage media and high speed
communication skill are developed dramatically; digital video has become one of
the most important elements in many applications such as education, news and
games.
 Text is obviously an important element in video. So extracting text appears as a
key clue for understanding contents of video and for instance for classifying
automatically some videos.
 Videotext detection and recognition has been identified as one of the key
components for the video retrieval and analysis system.
 Videotext detection and recognition can be used in many applications, such as
semantic video indexing, summarization, video surveillance and security etc.
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4. EXTRACTING TEXT FROM VIDEO
PreProcessing
HELLO
HELLO
HELLO
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5. TEXT EXTRACTION
 Text Extraction is a process by which we convert Printed document/Scanned Page
or Image in which text are available to ASCII Character that a Computer can
Recognize.
GENERAL APTITUDE
Computer Science
Electronics & Communication Engineering
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6. PROCESS OF TEXT EXTRACTION:
Pre
processing
RecognitionSegmentation
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7. GRAY SCALE
NOISE
REMOVAL
THRESHOLDING
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8. PREPROCESSING:
Gray Scale
• Noise Removal is used
to Enhance the Image
• For Enhancing We have
used median filter,for
the noise removal we
use low pass and high
pass filters.
Noise
• Edge Detection
• Dilate Image
• Detect Text Area Using
Histrogram
• Personal Thresholding
to Text Area
Thresholding
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2 3
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9. EDGING9

10. EDGE DETECTION USING CANNY10

11. DILATION AND TEXT AREA USING
HISTOGRAM
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12. SEGMENTATION
 Segmentation means fragmenting.Here the fragmenting is according to the line
to word,word to character.
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13.  In Recognition mainly it includes: Feature Extraction
Text Document
. . .
. . . .
. . . .. .
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14. ALGORITHM
 Take the frame
 RGB to Gray image
 defining the angles
 Edge detection
 Defining thresholds for vertical and horizontal text segmentation
 X and Y projection using threshold
 Binarization
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15. FLOWCHART15

16. ADVANTAGES:
 Less execution time
 Problem solving has improved
 Higher data recognition
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17. DISADVANTAGES:
 Background and text may be ambiguous.
 Text color may change: text can have arbitrary and non-uniform color.
 Background and text are sometimes reversed.
 Text may move.
 Unknown text size, position, orientation, and layout: captions lack the
structure usually associated with documents.
 Unconstrained background: the background can have colors similar to the
text colour. The background may include streaks that appear very similar to
character strokes.
 Low contrast: low bit-rate video compression can cause loss of contrast
between character strokes and the background
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18. APPLICATIONS:
 Banking (To read Credit Card)
 Libraries (To convert Scanned Page to Image)
 Govt. Sector (Form Processing)
 Used in Car Number Plate Recognition System
 Undesirable Text removal from images.
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19. FUTURE SCOPE:
 Future Scope We have implemented the project considering English language; it
can be further extended to other languages. If enlarged in future
implementations, it will largely improve the efficiency of the algorithm.
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20. BOOKS & REFERENCES
 A. Tognetti, F. Lorussi, R. Bartalesi, S. Quaglini, M. Tesconi, G. Zupone, and D. De Rossi,
“Wearable kinesthetic system for capturing and classifying upper limb gesture in post-
stroke rehabilitation,” J. Neuroeng. Rehabil., vol. 2, no. 1, p. 8, Mar. 2005.
 Chen, Datong, Bourlard, H., and Thiran J. P., 2001 “Text identification in complex
background using SVM”. IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, Proceedings of the 2001, 2, 8-14:621-626.
 JulindaGllavata, Ralph Ewerth and Bernd Freisleben, A Robust algorithm for Text
detection in images, Proceedings of the 3rd international symposium on Image
and Signal Processing and Analysis, 2003.
 http://matlabsproj.blogspot.nl/2012/06/extract-frames-from-video-matlab.html
 https://www.google.nl/search?q=matlab%20code%20on%20frames&rct=j
 http://www.divilabs.com/2013/11/extracting-saving-of-frames-from-video.html
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