Signer-independence finger alphabet recognition using discrete wavelet transform and area level run lengths

• Mobile Sign Language Recognition (SLR) system from backhand view is proposed.
• Signs are divided into two groups and applied appropriate tools for each group.
• Combination between 1D signal and DWT is used to differentiate fist signs.
• Block mask and quantized area distribution precisely recognize non-fist signs.

This paper proposes a method for finger alphabet recognition from backhand images with signer-independence. Input images that are divided into fist sign and non-fist sign groups should be analyzed and processed in different ways. Finger alphabets in the fist group are represented by a one-dimensional signal that represents the external hand boundaries. Its low and high frequency components are then extracted by discrete wavelet transform, which are key features for recognition. The non-fist sign images, which are radically digitized into a 20 × 20 block mask in terms of the hand geometry, due to the hand’s physical structure, can be recognized by the patterns of the occupied blocks. The experimental results show that the proposed method has a high likelihood of differentiating twenty-three static finger alphabets of backhand images. The proposed method reaches an improvement of 27.86% in recognition accuracy on a significant dataset of fist signs that includes multiple users, while the statistical distribution of the area level run length algorithm outperforms previous forehand approaches by 89.38% in recognition accuracy.