Hand gesture recognition method arriving great consideration in latest few years since of its manifoldness application and facility to interrelate by machine efficiently during human computer interaction. This paper mainly focuses on the survey on Hand Gesture Recognition. The hand gestures give a divide complementary modality to speech for express ones data. Hand gesture is the method of non-verbal communiqué for human beings for its freer expressions much more other than the body parts. Hand gesture detection has greater significance in scheme a competent human computer interaction method. This paper emphasis on different hand gesture approaches, technologies and applications.

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Review on Hand Gesture Recognition
Sindhu.K.M
M.Tech student, Dept of E&C,
Don Bosco Institute of Technology,
Sindhu.matad@gmail.com
Suresha.H.S
Associate Professor, Dept of E&C,
Don Bosco Institute of Technology,
srisuri75@gmail.com
Abstract: Hand gesture recognition method arriving
great consideration in latest few years since of its
manifoldness application and facility to interrelate by
machine efficiently during human computer
interaction. This paper mainly focuses on the survey on
Hand Gesture Recognition. The hand gestures give a
divide complementary modality to speech for express
ones data. Hand gesture is the method of non-verbal
communiqué for human beings for its freer expressions
much more other than the body parts. Hand gesture
detection has greater significance in scheme a
competent human computer interaction method. This
paper emphasis on different hand gesture approaches,
technologies and applications.
Keywords: Hand Gesture Recognition, Segmentation,
Feature Extraction and Classification
I. INTRODUCTION
India is diversified in culture, language
and religion. Since there is a great diversity among
Indian languages, the literature survey reports the
non-existence of standard forms of Indigenous Sign
Language (LSL) gestures. ISL alphabets are
derived from British Sign Language (BSL) and
French Sign Language (FSL). Because of these
problems, the standard database for the ISL /
gesture alphabet has not been developed so far.
Few research works has been carried out on ISL
recognition and interpretation through image
processing / vision techniques. But these are only
initial jobs proven with simple image processing
techniques and are not treated with real-time data.
The classification technique refers to the Euclidean
distance metric. Subsequently we propose a system
to translate the input speech to ISL that is shown
with the help of a 3D virtual human avatar. The
input to the system is the speech of the employee
who is in English.
The speech recognition module recognizes
speech and performs a text output. This text is then
passed to a parser module that tokenizes the string
and labels the part of the voice using a sample file.
The output of the analyzer is given to an eliminator
module that performs a reduction task by removing
unwanted elements and also the root form of the
verbs are found using the stemmer module. The
structural divergence of English and ISL is handled
by a phrase reordering module using the ISL
dictionary and the rule. This module generates ISL
brightness strings that can be reproduced through
virtual human 3D.
A 3D animation module creates animation
from motion-captured data. In this approach a lot of
3D model data is used which makes the system
clumsy and bulk. Attempt to machine static
translation as well as dynamic ISL gestures with
image processing features such as skin tone
detection space filter velocimetry and temporal
tracking is developed. The representation of the
power spectrum of each gesture is given as moving
images. Edge detection, cropping and boundary
tracking are used as characteristics for the
recognition process. These methods work well for
the static signs of ISL. They do not deal with the
dynamic, global and local movements of ISL
gestures. For example, the ISL signs of the letters
A-B, M-N, U-V look similar. It is sometimes
difficult for a human to correctly recognize the
sign. When it comes to computers, the inter-class
variability parameter must be considered
II. LITERATURE SURVEY
Giulio Marin et.al [01] introduces the two
various gesture recognition methods for Leap
Motion plus Kinect devices has been proposed.
Various feature sets are utilized to deal with
dissimilar nature of information provided with two
devices; Leap Motion gives a high level however
more imperfect information description though
Kinect gives the complete depth of map. Even if

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the information provided with Leap Motion is not
absolutely dependable, since several fingers may
not be identified, the planned set of the descriptions
and classification method permits attained a high-
quality in accuracy. The more absolute description
presented with depth map of Kinect permits
capturing other properties omitted in Leap Motion
yield by combine the two strategy a very high-
quality accuracy is attained. The experimental
results demonstrate that the task of each finger to
precise angular region lead to the substantial
enlarge of the recital.
J. Rekbai et.al [02] proposes an advance to
deal with inter-class uncertainty subject in ISL
alphabet detection. Through the assist of local-
global fmger group data and shape-texture
descriptions, accurate detection of every ISL
symbols have been attained for mutually static &
dynamic gesture. Conversely, suitable to less
steady life of PCBR descriptions, the correctness
slips downwards faintly in case of the dynamic
signals of ISL. The future potential work
concentrate on study of the dynamic nature of the
gestures below dissimilar circumstances.
Chao Xu et.al [04] explore that how smart
watch is utilized for gesture identification with
finger-writing. They demonstrated that the smart
watch sensors can correctly notice arm, dispense
and even finger gesture. It can also display that
watch identify the characters when addict writes on
the surface by her guide finger. Gesture
identification and finger- writing by smart watch is
utilized to produce new application for an
interacting by near devices and distantly controlling
it. Then, they are designing effective touch-screen
with methods to identify user's finger-writing in air
on the smartwatch sensor.
Pavlo Molchanov et.al [05] developed an
effectual process for energetic hand gesture
identification by 3D convolutional neural network.
This classifier utilizes the combined motion amount
of normalize the depth and picture gradient ideals;
with utilize spatio-temporal information
augmentation to evade over fitting. With means of
the extensive assessment, they established that
arrangement of the low and high declaration sub-
networks advances categorization accuracy
significantly. Further they established that the
proposed information augmentation method acts a
significant position in attaining superior
presentation.
Shalini Gupta et.al [06] introduces the new
multi-sensor systems that recognize dynamic
gesture of the drivers in the car. Preliminary
experiments show that the dual employ of the
color, short-range radar and depth sensors get better
accuracy, robustness, with power utilization of
gesture detection scheme. In future, they will
discover by using micro-Doppler signature
calculated by radar as the descriptions for gesture
identification. They also increase the revise to
larger information set of gesture more subject to
advance the simplification of DNN; also expand
the methodologies for constant online frame-wise
motion identification.
Yang Zhang et.al [07] presented a
wearable, low-cost and low power Electrical
Impedance Tomography scheme for hand gesture
identification. It process cross-sectional bio
impedance by 8 electrodes on wearer’s skin. By 28
all-pairs capacity, software will improve interior
impedance allocation, which can feed to hand
gesture classification. They assess two gesture of
sets (hand and pinch sets) with two body placement
(wrist and arm). User learns marks illustrate that
the advance can propose elevated correctness hand
gesture identification when the scheme is skilled on
wearer. Though, like mainly other bio-sensing
system, marks corrupt when scheme is re-worn at
presently time, or wear by other user.
ChenyangZhang et.al [08] proposes the
ovel discriminative 3D descrip-tor (H3DF) method
can effectively capture and replica rich surface
shape data of depth maps. Apply the orientation
normalization, forceful coding with concentric
spatial pooling, the H3DF descriptor is robust to
conversion, sight angle with scaling changes. Lo-
cal H3DF can also able to develop into intense
H3DF for form more local patterns. To tack lethet
enquire of energetic hand gesture and human action
identification as of the depth video sequence, the
two temporal addition methods are urbanized:
dynamic programming-based temporal partition
and N-gram-based method. The two methods are
applied to construct increased descriptors by robust
representative explanation. They have extensively
assessed the efficiency of anticipated H3DF
descriptor on 4 public datasets counting static hand
gesture identification from single depth picture,
dynamic hand gesture and human act identification
from depth sequence. Then experimental results
show that proposed method outperform or achieve

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similar accuracy to state-of-the-art for act and hand
gesture identification.
Nurettin Cag˘rı Kılıboz [10] presents the
easy yet powerful algorithm to identify and be
familiar with trajectory-based dynamic hand
gesture in actual time. The gestures can be
representing with the ordered series of directional
actions in the 2D space. Gesture information can
collected with a magnetic place tracker emotionally
involved to user hand, however the method also
appropriate to motion information gathered by
vision based methods, inertial motion capture
techniques or depth sensor. The motion information
in absolute place format changed to representation
through the motion capture stage.
III. HAND GESTURE RECOGNITION
Segmentation
Feature
Extraction
Classification
Input Hand
Gesture Image
Result
Performance
Fig.1: Proposed block diagram of hand gesture recognition
A. Hand Segmentation
Segmentation procedure is the first
progression for the recognizing hand gestures. This
is the method of separating the input picture (hand
gesture image) into areas divided by limitations.
The segmentation method depends on sort of
gesture, if it can be dynamic gesture then hand
gesture require to be situated and track, it can static
gesture (posture) input image is segmented only.
The hand must located initially, usually the
bounding box utilized to identify the depending on
skin color and next, the hand include to be track,
for track the hand there is two main methods; either
video seperated into frames and every frame
contain to be process alone, in that case the hand
frame can treated as the posture and segmented, or
by several tracking data like shape, skin color by
several filter. Fig.1 represents the general system
for hand gesture recognition.
B. Feature Extraction
The segmentation procedure leads to ideal
features extraction method and latter act and
significant role in doing well recognition
procedure. The features vector of segmented image
can extracted in various ways according to the
particular appliance. Different methods are applied
for representative the features can extracted.
Various methods utilized shape of hand while
others employed fingertips place, palm center, etc.
created 13 parameters as a feature vector, the
primary parameters represent the ratio feature of
bounding box of hand and rest 12 parameters are
mean ideals of the brightness pixels in image.
C. Gesture Classification

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Fig.2 Gesture Representation
There are various algorithms to notice
hand from an input image. Hand gesture
identification methods were updated by technology
changes. Based on this updates hand gesture
recognition approaches can be classified into
various categories is shows in above Fig.2. After
modelling and study of an input hand image,
gesture classification approaches are utilized to
identify the gesture. The recognition procedure
affected with proper assortment of the features
parameter and appropriate classification method.
For instance the edge detection or contour
operators cannot be utilized for gesture recognition
since lots of hand postures produced and can create
misclassification. The hand gesture is obtained in
discover the hand gesture as of image and
distinctive hand as of background from the
unwanted objects. Skin color provides an effectual
and efficient for hand detection. Segmentation
based skin color method applied for hand locate.
The recognition procedure can affected with the
proper assortment of gesture parameters of
descriptions and accuracy of its categorization.
IV. EXPECTED RESULTS
In gesture recognition, uses series of
images as the template. This form is especially easy
as compared to previous residual two methods.
Among the help of these gestures we can handle
the actions of hand; up, down, left and right is
shows in Fig.3.
Up Down

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Right Left
Fig.3: Hand gesture recognition
V. CONCLUSION
Hand gesture recognition is discovery its
application for nonverbal message among human
and computer, general fit person and physically
challenged people, 3D gaming, virtual reality etc.
With enlarge in applications, the gesture
recognition method stress lots of investigate in
various directions.
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