eSC and HOG-based adaptive HMM sign language identifying method

Abstract

The invention discloses an eSC and HOG-based adaptive HMM sign language identifying method which includes the steps of S1: conducting density-based sampling for the track of a sign language, extracting shape context characteristic, and combining with a pyramid processing method to obtaining eSC characteristics including space and time information, S2: for hand shape characteristics, extracting a rectangle frame containing a hand from image data and extracting HOG characteristics in the frame to realize description of the hand shape characteristics, S3: establishing an adaptive HMM model based on extracted eSC characteristics and HOG characteristics, and S4: for the data to be identified, extracting eSC characteristics and HOG characteristics through S1 and S2, and identifying the eSC characteristics and HOG characteristics extracted from the data to be identified through the adaptive HMM model to obtain the identifying model. Through the method, the correct rate of sign language identification is improved substantially.

Description

technical field [0001] The invention relates to the field of sign language recognition in multimedia technology, in particular to a sign language recognition method based on eSC and HOG adaptive HMM. Background technique [0002] In the field of sign language recognition, there are two crucial problems. One is how to obtain and design robust and efficient sign language action features, and the other is how to establish a robust recognition model for sign language action features. [0003] For the first question, scholars have introduced data glove sensors since the last century to record the position and deformation of each finger in detail. However, data gloves are not only expensive, but also require the tester to wear complex equipment. For this reason, some scholars have introduced color gloves to visually track and segment the hand shape according to the color of the glove, but it still requires the tester to wear it. In recent years, with the emergence of somatosenso...

AI Technical Summary

Problems solved by technology

Since there have been no recognized, practical, and robust sign language recognition features and methods, sign language recognition technology cannot be as practical as speech recognition technology today.

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