REAL-TIME SIGN LANGUAGE RECOGNITION SYSTEM

A. Background / Context Sign language is an important means of communication for many deaf and hard-of-hearing people. However, communication can be difficult when the other person does not understand the sign language being used. Computer vision and machine learning offer ways to recognize visible hand gestures and convert selected signs into readable text. A real-time recognition system processes camera frames continuously and provides feedback with limited delay. B. Problem Statement Many communication settings lack an interpreter or a shared sign language. Manual interpretation is not always immediately available, while text-based communication may interrupt natural interaction. A useful assistive system should detect hand gestures from a live camera stream, classify supported signs, and present results in a clear interface. Recognition can be affected by occlusion, background clutter, different users, and similar-looking signs. C. Objective / Aim The objective is to design a real-time sign language recognition system that captures video, detects hand landmarks or regions, extracts useful features, classifies supported signs, and displays the predicted label. The proposed modular design supports testing and future expansion of the vocabulary. D. Significance / Contribution The proposed system aims to support communication accessibility by converting a limited set of recognized signs into text. Its contribution is a clear, modular pipeline that can run on a standard computer with a camera and can be extended with additional signs, confidence feedback, or text-to-speech output. E. Paper Organization The remainder of this paper discusses related work, the proposed system and methodology, implementation choices, evaluation design, limitations, future scope, and conclusion.

A. Overview of Existing Research Research on sign language recognition includes sensor-based gloves, image-processing methods, hand-shape descriptors, deep-learning models, and landmark-based approaches. Camera-based systems are attractive because they do not require users to wear specialized hardware. Hand-landmark tools can represent fingers and joints as coordinates, which can be used as input to a classifier. B. Summary of Key Research Works Earlier approaches often used handcrafted features and classifiers such as support vector machines or random forests. More recent approaches use convolutional neural networks for image-based recognition and temporal models for sequences of signs. Landmark-based pipelines can reduce input dimensionality and may be practical for real-time prototypes, although performance depends on the dataset and the complexity of the signs. C. Identified Research Gap A key challenge is building a system that is responsive while remaining reliable across different signers, lighting conditions, camera positions, and backgrounds. Static hand poses do not represent every sign; some signs depend on movement, facial expression, or body posture. A prototype must clearly state which signs it supports and avoid implying that a small vocabulary provides full sign-language translation. D. Contribution of the Proposed Work This work proposes a modular camera-based system that detects hand landmarks, derives features, classifies supported signs, and displays the predicted label. The focus is on real-time interaction, low-cost hardware, transparent evaluation, and an architecture that can later support dynamic gestures and a larger vocabulary.

Fig. 2. Query classification and response workflow.

G. Advantages of the Proposed System

• Live Camera Input: captures gestures from a webcam.
• Hand Landmark Detection: identifies hand joints and finger positions.
• Real-Time Prediction: classifies supported gestures as frames arrive.
• Accessible Output: presents recognized signs as readable text.
• Modular Design: allows the detector, classifier, and interface to be updated separately.
• Future Extension: can add dynamic signs, regional sign vocabularies, and optional speech output.

A. Limitations

• Recognition may be unreliable under poor lighting, occlusion, cluttered backgrounds, or unusual camera angles.
• A model trained on a limited dataset may not generalize to new signers or regional variations.
• A static-pose classifier cannot fully recognize signs that depend on motion, facial expression, or body posture.
• The system recognizes only the labels represented in its training data and should not be described as a complete sign-language translator.
• Performance depends on the dataset, camera, hardware, preprocessing, model, and evaluation protocol.

B. Future Scope

• Expand the dataset with diverse signers and balanced examples for each class.
• Add temporal models for motion-based signs and sequence recognition.
• Explore speech output, sentence assembly, and multilingual interface options.
• Improve robustness to backgrounds, lighting, and partial hand occlusion.
• Evaluate on independent users and report per-class metrics and latency.
• Extend recognition to two-hand gestures, facial cues, and broader sign vocabularies where appropriate.

The proposed Real-Time Sign Language Recognition System combines camera input, hand-landmark detection, feature extraction, classification, and text output in a modular pipeline. It offers a practical foundation for recognizing a defined set of signs with accessible, low-cost hardware. Its usefulness depends on careful dataset design, real-time implementation, and honest evaluation. Before submission, the system should be implemented and tested, and measured results should replace placeholders. The paper should clearly describe the supported sign vocabulary, dataset, training procedure, hardware, limitations, and evaluation protocol. Future work may extend the prototype to dynamic signs, broader vocabularies, and speech output.