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ABSTRACT
Sign language is the only tool of communication for the person who is not able to speak and hear anything. Sign language is a boon for the physically challenged people to express their thoughts and emotion. In this work, a novel scheme of sign language recognition has been proposed for identifying the alphabets and gestures in sign
language. With the help of computer vision and neural networks we can detect the
signs and give the respective text output.
KeyWord: Sign LanguageRecognition1, Convolution Neural Network2, Image
Processing3, Edge Detection4, Hand Gesture Recogniton5
INTRODUCTION
Speech impaired people use hand signs and gestures to communicate. Normal
people face difficulty in understanding their language. Hence there is a need of a
system which recognizes the different signs, gestures and conveys the information to
the normal people. It bridges the gap between physically challenged people and
normal people.
IMAGEPROCESSING
Image processing is a method to perform some operations on an image, in
order to get an enhanced image or to extract some useful information from it. It is a
type of signal processing in which input is an image and output may be image or
characteristics/features associated with that image. Nowadays, image processing is
among rapidly growing technologies. It forms core research area within engineering
and computer science disciplines too.
Image processing basically includes the following three steps:
- Importing the image via image acquisition tools.
- Analysing and manipulating the image.
- Output in which result can be altered image or report that is based on image analysis.
There are two types of methods used for image processing namely, analogue
and digital image processing. Analogue image processing can be used for the hard
copies like printouts and photographs. Image analysts use various fundamentals of
interpretation while using these visual techniques. Digital image processing techniques
help in manipulation of the digital images by using computers. The three general
phases that all types of data have to undergo while using digital technique are preprocessing, enhancement, and display, information extraction.
Digital image processing:
Digital image processing consists of the manipulation of images using digital
computers. Its use has been increasing exponentially in the last decades. Its
applications range from medicine to entertainment, passing by geological processing
and remote sensing. Multimedia systems, one of the pillars of the modern information
society, rely heavily on digital image processing.
Digital image processing consists of the manipulation of those finite precision
numbers. The processing of digital images can be divided into several classes: image
enhancement, image restoration, image analysis, and image compression. In image
enhancement, an image is manipulated, mostly by heuristic techniques, so that a
human viewer can extract useful information from it.
Digital image processing is to process images by computer. Digital image
processing can be defined as subjecting a numerical representation of an object to a
series of operations in order to obtain a desired result. Digital image processing
consists of the conversion of a physical image into a corresponding digital image and
the extraction of significant information from the digital image by applying various
algorithms.
Pattern recognition: On the basis of image processing, it is necessary to separate
objects from images by pattern recognition technology, then to identify and classify
these objects through technologies provided by statistical decision theory. Under the
conditions that an image includes several objects, the pattern recognition consists of
three phases, as shown in Fig.
Fig1.1: Phases of pattern recognition
The first phase includes the image segmentation and object separation. In this
phase, different objects are detected and separate from other background. The second
phase is the feature extraction. In this phase, objects are measured. The measuring
feature is to quantitatively estimate some important features of objects, and a group of
the features are combined to make up a feature vector during feature extraction. The
third phase is classification. In this phase, the output is just a decision to determine
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which category every object belongs to. Therefore, for pattern recognition, what input
are images and what output are object types and structural analysis of images. The
structural analysis is a description of images in order to correctly understand and judge
for the important information of images
