Handshape Recognition in an Educational Game for Finger Alphabet Practicing

Kapuscinski, Tomasz

doi:10.1007/978-3-031-09680-8_7

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Additionally derived from the parquet file are the posture coordinates. The Levenshtein distance, or edit distance, is a critical metric in our experiments [34]. It quantifies the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one word into another, serving as an effective gauge of our system's accuracy [35].…”

Section: Resultsmentioning

confidence: 99%

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Kozhamkulova,

Nurlybaeva,

Kuntunova

et al. 2023

IJACSA

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This paper presents a novel approach to fingerspelling recognition in real-time, utilizing a twodimensional Convolutional Neural Network (2D CNN). Existing recognition systems often fall short in real-world conditions due to variations in illumination, background, and user-specific characteristics. Our method addresses these challenges, delivering significantly improved performance. Leveraging a robust 2D CNN architecture, the system processes image sequences representing the dynamic nature of fingerspelling. We focus on low-level spatial features and temporal patterns, thereby ensuring a more accurate capture of the intricate nuances of fingerspelling. Additionally, the incorporation of real-time video feed enhances the system's responsiveness. We validate our model through comprehensive experiments, showcasing its superior recognition rate over current methods. In scenarios involving varied lighting, different backgrounds, and distinct user behaviors, our system consistently outperforms. The findings demonstrate that the 2D CNN approach holds promise in improving fingerspelling recognition, thereby aiding communication for the hearing-impaired community. This work paves the way for further exploration of deep learning applications in real-time sign language interpretation. This research bears profound implications for accessibility and inclusivity in communication technology.

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Section: Resultsmentioning

confidence: 99%

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Kozhamkulova,

Nurlybaeva,

Kuntunova

et al. 2023

IJACSA

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“…Works are also described in which skeletal hand models are used to build educational games for learning the finger alphabet or sign language [20,[32][33][34].…”

Section: Related Workmentioning

confidence: 99%

A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition

Warchoł,

Kapuściński

2023

Applied Sciences

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Automatic recognition of hand postures is an important research topic with many applications, e.g., communication support for deaf people. In this paper, we present a novel four-stage, Mahalanobis-distance-based method for hand posture recognition using skeletal data. The proposed method is based on a two-stage classification algorithm with two additional stages related to joint preprocessing (normalization) and a rule-based system, specific to hand shapes that the algorithm is meant to classify. The method achieves superior effectiveness on two benchmark datasets, the first of which was created by us for the purpose of this work, while the second is a well-known and publicly available dataset. The method’s recognition rate measured by leave-one-subject-out cross-validation tests is 94.69% on the first dataset and 97.44% on the second. Experiments, including comparison with other state-of-the-art methods and ablation studies related to classification accuracy and time, confirm the effectiveness of our approach.

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Handshape Recognition in an Educational Game for Finger Alphabet Practicing

Cited by 2 publications

References 28 publications

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

Two Dimensional Deep CNN Model for Vision-based Fingerspelling Recognition System

A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition

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