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A hand gesture recognition system provide a natural, innovative and modern way of non verbal communication. It has a wide area of application in human computer interaction and sign language. The intention of this paper is to discuss a novel approach of hand gesture recognition based on detection of some shape based features. The setup consist of a single camera to capture the gesture formed by the user and take this hand image as an input to the proposed algorithm. The overall algorithm divided into four main steps, which includes segmentation, orientation detection, feature extraction and classification. The proposed algorithm is independent of user characteristics. It does not require any kind of training of sample data.
- The primary goal our project is to detect the Hand Gesture Recognition
- In our proposed system we compute texture color feature for compute the similarity between query and database images. This integrated approach will reduce the output results to a certain levels based on the user threshold value.
- The clustering(i.e K mean cluster) technique cluster the output images and select one representative image from each clusters.
- A third goal is to evaluate their performance with regard to speed and accuracy. These properties were chosen because they have the greatest impact on the implementation effort.
- A final goal has been to design and implement an algorithm. This should be done in high-level language or Matlab.
- Threshold method
- K means clustering
- Manual analysis – time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors.
- Difficulties are there to find optimal gradient
- Poor Edge detection.
- Manual segmentation
We recognize the hand gesture (HRI) requires media for communication which can be both understood by robot and easily done by human, especially to help deaf people, patient, and old people, therefore gesture recognition as communication media is needed to give order to Robot. Using Fuzzy c mean clustering and CNN classifier and provides the output in form of text as well as audio.
1)Apriori specification of the number of clusters.
2) With lower value of? ? we get the better result but at the expense of ?more number of iteratio3)?Euclidean distance measures can unequally weight underlying factors.
4) Output is obtained in form of both text and speech.
- Computer vision techniques
- Automatic sign recognition technique
- Anaconda Navigator
- Python built-in modules Robotics
- SOFTWARE REQUIREMENTS