A SIGN LANGUAGE RECOGNIZER

Abstract

The local sign language recognizer employs advanced computer vision and machine learning techniques to interpret gestures from local sign languages, translating them into text or speech. This invention addresses the need for recognition systems tailored to regional sign languages, which exhibit unique vocabulary and grammar. By utilizing a region-specific dataset, the system ensures accurate gesture recognition and promotes inclusivity in communication for the deaf and hard of hearing community. The proposed automated process includes data collection, preprocessing, feature extraction, model training, and real-time recognition, demonstrating promising accuracy in preliminary tests. This technology aims to bridge the communication gap and enhance accessibility for users of local sign languages, ultimately fostering better understanding and interaction between deaf individuals and society at large.

Specification

Description:The present invention pertains to the domain of assistive technologies, specifically within the fields of computer vision, machine learning, and natural language processing. It focuses on the recognition of sign languages used by individuals with hearing impairments. The invention aims to enhance communication accessibility for the deaf and hard of hearing community by developing a tailored recognition system for local sign languages, thereby promoting inclusivity and facilitating better interaction between deaf individuals and the broader public.
BACKGROUND OF THE INVENTION
Sign language is a vital means of communication for many individuals with hearing impairments. For a significant portion of the deaf population, sign language serves as their primary form of expression and interaction with the world around them. However, existing sign language recognition technologies predominantly focus on widely recognized sign languages, such as American Sign Language (ASL) or British Sign Language (BSL). This oversight has left a substantial gap for speakers of regional or local sign languages, which often possess unique vocabulary, grammar, and cultural context.
The lack of recognition systems tailored to these local sign languages presents significant challenges in accessibility and communication. It limits the ability of deaf individuals to effectively convey their thoughts and emotions in environments that may not be equipped to understand their specific sign language. Furthermore, many public services, educational institutions, and social environments do not cater to these unique linguistic characteristics, further alienating users of local sign languages.
Recent advancements in machine learning and computer vision technologies present an opportunity to bridge this communication gap. By leveraging these technologies, it is possible to develop a robust sign language recognition system that can accurately interpret and translate the gestures associated with local sign languages into text or speech. The development of such a system would represent a significant advancement in assistive communication technologies, promoting inclusivity and enhancing the quality of life for the deaf and hard of hearing community.
OBJECTS OF THE INVENTION
An object of the present invention is to create an effective sign language recognition system that specifically addresses the needs of local sign languages, ensuring accurate interpretation and translation of gestures unique to regional dialects.
Another object of the present invention is to leverage state-of-the-art computer vision and machine learning techniques to enhance the accuracy and reliability of gesture recognition, making it possible to translate complex signs into understandable text or speech.
Yet another object of the present invention is to gather a comprehensive dataset that reflects the unique vocabulary and grammatical structures of various local sign languages, ensuring the training process is relevant and effective.
Another object of the present invention is to enable seamless communication between deaf and hard of hearing individuals and the broader public, thereby enhancing social inclusion and interaction in diverse settings.
Another object of the present invention is to address the underrepresentation of local sign languages in existing recognition technologies, thereby providing a foundation for the development of more inclusive sign language solutions that cater to a wider range of users.
Another object of the present invention is to incorporate a feedback mechanism that allows users to provide input on recognition accuracy, ensuring the continuous improvement of the system over time.
SUMMARY OF THE INVENTION
The Local Sign Language Recognizer presents a novel approach to sign language recognition by utilizing advanced computer vision and machine learning techniques to interpret gestures from local sign languages and convert them into text or speech. This invention addresses the significant need for recognition systems that cater to regional sign languages, which often possess unique linguistic features that differ from major sign languages.
The invention comprises an automated process that includes several critical stages: data collection, preprocessing, feature extraction, model training, real-time recognition, and user feedback integration. Initially, a comprehensive dataset is collected, comprising video recordings of individuals performing gestures in various local sign languages. This dataset is processed to enhance quality and isolate relevant features such as hand movements and facial expressions.
Next, machine learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are trained on the processed dataset to recognize specific gestures and correlate them with corresponding words or phrases. The final product is a user-friendly application that allows real-time recognition of gestures, enabling immediate translation into text or speech. Preliminary tests of the system have shown promising accuracy rates, emphasizing its potential to improve communication accessibility for the deaf and hard of hearing community.
In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
DETAILED DESCRIPTION OF THE INVENTION
An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.
The sign language recognizer is designed to provide an intuitive and effective solution for recognizing local sign languages through a systematic and comprehensive approach. The invention begins with data collection, which forms the foundation of the recognition system. A comprehensive dataset is compiled, capturing the nuances of local sign languages through diverse video recordings of individuals performing sign language gestures in various contexts, such as conversations, educational settings, and everyday interactions. This dataset encompasses different signing styles, regional dialects, and age groups, ensuring a rich representation of the local sign language vocabulary.
Once the data is collected, it undergoes a preprocessing stage to enhance the quality of the input data. This process involves several techniques, including background subtraction to isolate the signer from the background, normalization to adjust video quality parameters such as brightness and contrast for consistency, and segmentation to divide video clips into smaller segments that isolate individual gestures for more effective analysis. This preprocessing ensures that the system can accurately focus on the relevant movements and expressions necessary for recognition.
Following preprocessing, the next crucial step is feature extraction, where the system identifies and captures the relevant characteristics of the gestures performed. This stage employs optical flow analysis to track the movement of hands and body parts over time, allowing the system to capture the dynamics of gestures accurately. Additionally, key point detection techniques are utilized to identify specific points on the hands and face, such as fingertips, joints, and eyes, which are crucial for understanding gestures and facial expressions.
The extracted features are then fed into a series of machine learning models for training. These models include Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), both of which are particularly effective for processing image and video data. CNNs can learn hierarchical features and patterns from the visual data, while RNNs capture the temporal dynamics of sign language, recognizing that gestures often involve sequences of movements over time. Through this combination, the models become capable of accurately interpreting the specific gestures associated with local sign languages.
Once the models are trained, they are integrated into a user-friendly application designed for real-time gesture recognition. Users can perform sign language gestures in front of a camera, and the system processes the input video, interpreting and translating the gestures into text or speech instantaneously. This application is designed to be accessible across various devices, including smartphones, tablets, and computers, allowing widespread use in diverse environments. By facilitating immediate translation of sign language, the system significantly enhances communication accessibility for deaf individuals, bridging the gap between them and the hearing community.
An essential component of the Local Sign Language Recognizer is the feedback mechanism that allows users to provide corrections for any misinterpretations made by the system. This feedback is critical for continuously refining and improving the accuracy of the machine learning models over time. The system can learn from user interactions, adapting to individual signing styles and regional variations, ultimately enhancing its recognition capabilities.
The deployment of the Local Sign Language Recognizer is envisioned in various contexts, including educational institutions, public services, and social settings, thereby fostering better communication between deaf individuals and the hearing community. The application aims to enhance inclusivity and understanding, providing a robust solution that addresses the unique linguistic characteristics of local sign languages. Through this comprehensive approach, the Local Sign Language Recognizer not only enhances communication accessibility for the deaf and hard of hearing community but also promotes a more inclusive society.
The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.
, Claims:1. A sign language recognizer system comprising:
a data collection module configured to gather video recordings of individuals performing gestures in various local sign languages;
a preprocessing module configured to enhance the quality of input video data through techniques including background subtraction, normalization, and segmentation;
a feature extraction module that utilizes optical flow analysis and key point detection to identify and track relevant hand movements and facial expressions;
a machine learning module that employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) trained on extracted features to recognize and interpret gestures;
a real-time recognition module that translates recognized gestures into text or speech;
a user feedback module that allows users to provide corrections for misinterpreted gestures, enabling continuous improvement of the recognition accuracy.

2. A method for recognizing local sign languages, comprising the steps of:
a) collecting a comprehensive dataset of video recordings of individuals performing gestures in various local sign languages;
b) preprocessing the collected video data to enhance quality through background subtraction, normalization, and segmentation;
c) extracting relevant features from the pre-processed video data using optical flow analysis and key point detection;
d) training machine learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), on the extracted features to recognize specific gestures;
e) implementing real-time recognition of gestures through a user-friendly application that translates gestures into text or speech;
f) incorporating user feedback to correct misinterpretations, thereby continuously improving the model's accuracy over time.

3. The system as claimed in claim 1, further comprising a feedback module that collects user corrections for misinterpreted gestures and utilizes the corrections to retrain the machine learning model.

4. The system as claimed in claim 1, wherein the data collection module is configured to record video in various contextual settings to enhance the diversity of the captured gestures.

5. The system as claimed in claim 1, wherein the feature extraction module further extracts temporal features to represent the duration and timing of the gestures.

6. The method as claimed in claim 2, wherein the capturing step further includes recruiting participants from diverse backgrounds to ensure a wide range of signing styles and dialects in the dataset.

7. The method as claimed in claim 2, wherein the preprocessing step further includes normalizing the video recordings to achieve consistency in lighting and resolution across the dataset.

8. The method as claimed in claim 2, further comprising the step of providing a feedback mechanism that allows users to indicate misinterpretations of gestures, thereby enhancing the accuracy of the machine learning model through retraining.

9. The method as claimed in claim 2, wherein the deploying step includes integrating the trained model into various platforms, including educational institutions, public services, and workplace environments, to enhance accessibility for users of local sign languages.

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