describe a sign language recognizer which can interpret deaf sign language to English or Bengali text.

1. An intelligent approach to recognize sign language for deaf and dumb people of the world

2. Dedication
First of all we would like to remember the
deaf and dumb people of the world for
whom we tried to develop a Sign language
Recognizer (SLR).

3. Outline
• Sign language
• SLR & its necessity
• Helping process of SLR
• Working procedure of SLR
• Block Diagram of SLR
• BP training time & graph
• Recognition accuracy
• Limitations
• Future plan
• Papers

4. What is Sign Language ??
 Communicating language
used primarily by deaf people.
 Uses different medium such
as hands, face, or eyes rather
than vocal tract or ears for
communication purpose.
Communication using sign language

5. What is SLR ??
Sign language recognizer (SLR) is a tool for
recognizing sign language of deaf and dumb
people of the world.

6. Why we need SLR ??
Problems:
• About 2 million people are deaf in our world
• They are deprived from various social
activities
• They are under-estimated to our society
• Communication problem

7. Continued..
Proposed Solution: SLR
SLR can be a desirable interpreter which can help
both the community general and deaf.

8. How SLR help ?? An Example.....
Suppose a deaf customer
went to a shop. She is ??
trying to express her
demands to the
shopkeeper using sign
language but the
shopkeeper can not
understand her demands. shopkeeper Deaf customer

9. Continued..
SLR brings the solution for this problem>>
• SLR capture signs shown by deaf man
• Convert the signs to text
• This text is shown to shopkeeper
Now the shopkeeper can understand the deaf man’s demands

10. Continued..
Sign to text conversion using SLR
Sign Converted text

11. Continued..
Text to sign conversion
When shopkeeper replied to the deaf customer SLR
• Convert text to sign
• This sign is shown to the deaf customer
Now the deaf man can understand the shopkeeper’s speeches

12. Continued..
Text to sign conversion using SLR
Shopkeeper speech/text Sign

13. Text to Sign Conversion

14. Process
Collecting Text
• Text from the writing
place are collected
Separate each letters
Showing sign

15. Continued..
Collecting Text
• From the sentences
each letter are
Separate each letter separated and put into
an array.
Showing sign

16. Continued..
Collecting Text
• For each letter a
predefined sign image
Separate each letters are shown.
Showing sign

17. Sign to Text Conversion

18. How SLR works ??
Image processing &
sign detection
Normalization
Sign recognition
Sign to text conversion

19. Continued..
Image processing &
sign detection
• Image capture
• Skin color detection
Normalization
Sign recognition
Sign to text conversion

20. Continued..
Image processing &
sign detection
• Hand gesture detection
Normalization • Sign detection
Sign recognition
Sign to text conversion

21. Continued..
Image processing &
sign detection
• Reducing image
size
Normalization
Sign recognition
Sign to text conversion
200x200 30x33

22. Continued..
Image processing &
sign detection
• Backpropagation
implementation
Normalization
Sign recognition
Sign to text conversion

23. Continued..
Image processing &
sign detection • Converting sign
language to Bengali
or English text
Normalization
Sign recognition
v
Sign to text conversion

24. Block diagram of the SLR

25. BP Training
Figure: Training error versus number of iteration

26. Training time for BP
Training
Input size of pixel Time
(min)
30*33 1.5
45*48 2.8
60*63 3.7
We have used 50 signs as training input where each
sign has 5 samples that make 50 x 5 = 250 samples.

27. Recognition Accuracy
Avg.
No. of input Accuracy
(%)
10 74
20 65
30 60

28. Limitations
• Due to brightness and contrast
sometimes webcam can hardly detect
the expected skin color.
• Because of the similarity of tracking
environment background color and skin
color the SLR gets unexpected pixels.

29. Continued..
• Due to almost similar pattern its become
hard to take decision.

30. Continued..

31. Future Plan
• Real time word recognition of ASL & BSL
• Implementing neural network Ensembles
• Implementing Genetic algorithm for sign
recognition

32. Required Tools
• Visual studio 2008
• XML
• Avro Keyboard installed
• Aforge .Net
• Open CV
• Webcam

33. References
 http://www.lifeprint.com/
 http://engineeringproject2011.webs.com/
 www.c-sharpcorner.com
 www.codeproject.com
 http://en.wikipedia.org
 www.aforgenet.com

34. Published papers
1. Bikash Chandra Karmokar, Kazi Md. RokibulAlam, Md.
KibriaSiddiquee, “An intelligent approach to recognize touchless
written Bengali characters”, International Conference on
Informatics, Electronics & Vision (ICIEV), ISSN: 2226-2105, 2012,
Dhaka, Bangladesh
2. Kazi Md. RokibulAlam, Bikash Chandra Karmokar, Md.
KibriaSiddiquee, “A comparison of constructive and pruning
algorithms to design neural networks”, Indian Journal of
Computer Science and Engineering (IJCSE), ISSN : 0976-5166 Vol.
2 No. 3 Jun-Jul 2011