This document discusses dynamic hand gesture recognition using human computer interaction. It proposes using a camera worn by hearing impaired users to capture hand gestures, which would then be processed on a computer using image processing techniques to recognize the gestures and map them to speech output. The system aims to help develop a prototype that can automatically recognize sign language gestures in real-time and translate them to voice. It reviews several previous works on sign language and gesture recognition and their limitations to motivate the proposed approach.

1. “Dynamic Hand Gesture Recognition using Human
Computer Interaction”
Guided By Presented By
Prof. Santosh Banoth Kunika S. Barai
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2.  ABSTRACT
 INTRODUCTION
 LITERATURE REVIEW
 PROBLEM DEFINITION
 EXISTING SYSTEM
 PROPOSED SCHEME
 BLOCK DIAGRAM
 REQUIREMENT
 ADVANTAGES
 APPLICATION
 FUTURE SCOPE
 REFERENCE
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3. A sign language recognition system is required to use
information from both global features, such as hand movement
and location and local features; such as hand shape and
orientation. Sign Language is the most natural and expressive
way for the hearing impaired. Proper hand segmentation from
the background and other body parts of the video is the
primary requirement for the design of a hand-gesture based
application. These video frames can be captured from a low
cost webcam (camera) for use in a vision based gesture
recognition technique.HCI can then help to control operation
of system.
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4.  The use of hand gestures is a natural way of communication
and has lead to many research efforts aiming for the
development of intelligent human interaction systems.
 A lot of work has been done in the field of automation of sign
language interpretation to make systems that effectively
translate signs i.e. hand gestures into speech or text
 This system was inspired by the special group of people who
have difficulties communicate in verbal form
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5. 29/10/2014 5

6.  In paper[1] to test and evaluate the system, k-NN and SVM
classifiers with different configurations have been employed.
 In paper[2] the research has deal with the sign language
posture and identified the sign and converted that sign into
text.
 In paper[3] the gloved hand tracking is compared with free
hand tracking algorithm is not dependent on any background
and lighting conditions.
 In paper[4] the low training times and low memory
consumption is given for better accuracy.
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7.  In [1] they require dark background to recognition of hand
posture.
 In [2] the recognition of some alphabates out of 26 were able
to convert in text.
 In [3] if color of the glove or finger cap is same with body
color then it is hard to track the posture.
 This allows to repeat the training procedure of MLRF due to
which different postures merge together and hard to
recognition.
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Our aim is to use the camera which is supposed to be worn by the
hearing impaired people.
The second functional block and the main block is the personal
computer where the complete image processing is carried out.
Finally the output served by the computer in the form of recognized
gesture is then corresponds to a particular speech track which is
played through the speaker.

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10.  MATLB
 A good confriguation of computer system.
 Low cost web cam include in computer system.
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11.  Non-verbal communication can be used for interaction with
computer system.
 This research will help to develop a system prototype that
automatically helps to recognize sign languages of the signer
and translate them into voice in real time.
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12.  Real time sign language.
 Security purpose.
 For authentication.
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13. Gesture recognition is new advanced topic used to increase
the human computer interaction. We are recognizing the hand
postures and performing different operation. In future head
moment can also be recognize for interaction. The Gesture
Controlled Robot System gives an alternative way of
controlling robots, household appliances and electronic
devices.
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14. 1) Ghassem Tofighi, Anastasios N. Venetsanopoulos, Kaamran Raahemifar,
Soosan Beheshti, Helia Mohammadi; Hand Posture Recognition Using K-
NN and Support Vector Machine Classifiers Evaluated on Our Proposed
HandReader Dataset;DSP 2013 IEEE
2) Matheesha Fernando, Janaka Wijayanayaka; Low cost approach for Real
Time Sign Language Recognition; 2013 IEEE 8th International Conference
on Industrial and Information Systems, ICIIS 2013, Aug. 18-20, 2013, Sri
Lanka
3) Dharani Mazumdar , Anjan Kumar Talukdar, Kandarpa Kumar Sarma;
Gloved and Free Hand Tracking based Hand Gesture Recognition;
ICETACS 2013
4) Alina Kuznetsova, Laura Leal-Taix´e, Bodo Rosenhahn; Real-time sign
language recognition using a consumer depth camera;2012 International
conference; Coimbatore, India.
5) P Raghu veera Chowdary, Image processing algorithm for gesture
recognition using matlab” , 2014 IEEE(ICACCCT)
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15. THANK YOU
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