PenSLR: Persian end-to-end Sign Language Recognition Using Ensembling

TL;DR

PenSLR is an innovative end-to-end Persian Sign Language Recognition system that uses ensembling and deep learning on sensor data, achieving high accuracy and introducing a new dataset for comprehensive sign language interpretation.

Contribution

The paper introduces a novel ensembling technique using Star Alignment and a new PSL dataset, advancing end-to-end sign language recognition for Persian.

Findings

Achieved 94.58% word accuracy in subject-independent setup

Ensembling improved word accuracy by up to 1.32%

Enhanced sentence-level accuracy by up to 4.00%

Abstract

Sign Language Recognition (SLR) is a fast-growing field that aims to fill the communication gaps between the hearing-impaired and people without hearing loss. Existing solutions for Persian Sign Language (PSL) are limited to word-level interpretations, underscoring the need for more advanced and comprehensive solutions. Moreover, previous work on other languages mainly focuses on manipulating the neural network architectures or hardware configurations instead of benefiting from the aggregated results of multiple models. In this paper, we introduce PenSLR, a glove-based sign language system consisting of an Inertial Measurement Unit (IMU) and five flexible sensors powered by a deep learning framework capable of predicting variable-length sequences. We achieve this in an end-to-end manner by leveraging the Connectionist Temporal Classification (CTC) loss function, eliminating the need for segmentation of input signals. To further enhance its capabilities, we propose a novel ensembling technique by leveraging a multiple sequence alignment algorithm known as Star Alignment. Furthermore, we introduce a new PSL dataset, including 16 PSL signs with more than 3000 time-series samples in total. We utilize this dataset to evaluate the performance of our system based on four word-level and sentence-level metrics. Our evaluations show that PenSLR achieves a remarkable word accuracy of 94.58% and 96.70% in subject-independent and subject-dependent setups, respectively. These achievements are attributable to our ensembling algorithm, which not only boosts the word-level performance by 0.51% and 1.32% in the respective scenarios but also yields significant enhancements of 1.46% and 4.00%, respectively, in sentence-level accuracy.