Manual alphabet identification method based on Leap Motion

Abstract

The invention discloses a manual alphabet identification method based on Leap Motion. The method comprises steps of acquiring palm and wrist bone articulation point 3D coordinate information through a depth camera Leap Motion device; performing correlative calculation on the 3D coordinate information so as to obtain bending angle information of hand joints; acquiring hand type feature information and wrist bending degree feature information through feature processing on the angle information; calculating the Euclidean distance between the acquired feature information and templates; and identifying manual alphabets according to a maximum probability criterion, a nearest neighbor criterion and a successive frame flow result consistent principle. By employing the method, the Chinese manual alphabets can be effectively and rapidly identified, manual alphabet elements are relatively independent, and sign language can be identified in a real time manner through the identification of manual alphabet continuous sequences. By employing the method, the sign language based on the manual alphabets can be identified in a real time manner, so that the deaf can effectively communicate with others by the use of a wearable device.

Description

technical field [0001] The invention relates to the field of human-computer intelligent interaction, in particular to a sign language letter recognition method based on a depth camera device Leap Motion. Background technique [0002] Sign language arose out of the communication needs of the deaf, and it has been gradually accepted as a language of the deaf. Sign language includes finger language and sign language. Sign language is to use finger changes and hand shapes to represent letters, and spell out words in turn according to the order of pinyin; Chinese sign language letters can represent ten numbers and 26 common letters, as well as commonly used letter combinations in Chinese, including ZH, CH, SH, NG, etc., so that every word spoken by ordinary people can be accurately expressed in sign language without ambiguity; [0003] At present, sign language recognition devices at home and abroad are mainly divided into wearable devices (data gloves, position trackers, accel...

AI Technical Summary

Problems solved by technology

This method has good human-computer interaction. However, due to the strong dependence of this method on external conditions such as light and background, it is necessary to identify people wearing specific colors when extracting features such as hand positions, hand shapes, and motion trajectories. Gloves and clothing with specific colors assist positioning and segmentation, so the method based on visual recognition is easily affected by environmental factors such as background, lighting, and camera position, and has limitations.

[0004] The Kinect-based sign language recognition system uses the Kinect device with a fixed viewing angle to obtain the feature information of the absolute spatial position coordinates, which contains a large number of upper body features and visually high-dimensional depth information of the hands, which is computationally expensive and inconvenient to carry. , cannot satisfy the interactive naturalness

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