International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 05 | May 2026
p-ISSN: 2395-0072
www.irjet.net
Sign Language Interpreter and Conversion to Text Shraddha Anil Sarode¹, Swarali Prakash Zore², Akshada Rajendra Bhosale³ ¹²³Department of Computer Science and Engineering(Artificial Intelligence and Data Science) Padmabhooshan Vasantraodada Patil Institute of Technology, Maharashtra, India Guide: Prof. Mrs. M. R. Khare ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - This paper introduces a real-time 2. PROBLEM STATEMENT Sign language interpretation system that converts hand gestures into text to support communication between individuals with hearing or speech impairments and healthcare professionals. Using computer vision and deep learning techniques, the system captures gestures from live video input and translates them into meaningful text instantly. This approach helps minimize communication barriers in clinical settings, enabling clearer interaction and improved patient care. The proposed system contributes to enhancing accessibility and promoting more inclusive healthcare services.
In hospitals, clear communication between doctors and patients is very important. But for patients who use sign language, expressing their symptoms, pain, or concerns can be difficult when doctors do not understand sign language. At the same time, doctors may find it challenging to explain diagnoses, prescriptions, or instructions in a way the patient can understand. Because of this gap, important information can be misunderstood, which may affect treatment and patient comfort.
Keywords- Sign Language Recognition, Healthcare Communication, Gesture Recognition, Sign-to-Text Conversion, Text-to-Sign Conversion, Real-Time Communication, Real-Time Communication.
This project focuses on creating a two-way communication system using sign language. The system will understand a patient’s hand gestures and convert them into text for the doctor. It will also allow doctors to type messages, which will then be shown as sign language gestures for the patient.
1. INTRODUCTION Communication plays a vital role in healthcare, as it directly affects diagnosis, treatment, and overall patient care. However, for individuals with hearing or speech impairments, communicating with doctors can be difficult and sometimes stressful. Since most healthcare professionals are not familiar with sign language, this often creates a gap that can lead to misunderstandings and delays in treatment.
The aim is to make communication in healthcare simpler, faster, and more accessible, so that every patient can clearly express themselves and understand their doctor without barriers.
3. OBJECTIVES
With advancements in technology, especially in computer vision and deep learning, it is now possible to develop systems that can understand and interpret human gestures. Sign language recognition systems aim to reduce this communication barrier by converting hand gestures into text, making interactions smoother and more effective.
The main objective of this project is to create a smart and reliable two-way communication system that helps doctors and patients communicate easily using sign language. The system is developed to reduce communication difficulties faced by people with hearing or speech impairments during medical consultations and treatment.
This paper presents a real-time Sign Language Interpretation and Conversion to Text system designed to assist in healthcare settings. The system captures hand gestures using a camera, processes them using deep learning models, and converts them into readable text instantly. By doing so, it helps improve communication, enhances accessibility, and supports a more inclusive healthcare environment.
The specific objectives of the proposed system are as follows: To identify and understand sign language gestures made by patients in real time. To convert patient gestures into readable text so that doctors can understand the message clearly. To transform the doctor’s typed text into sign language gestures for patients. To reduce misunderstandings and improve communication in hospitals and clinics.
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