FrGramm is a computational grammar fragment of French in the LFG formalism. It is implemented in the Xerox Linguistic Environment - XLE. To compile a parser from the grammar and parse sentences in French, you need XLE: http://www2.parc.com/isl/groups/nltt/xle/. FrGramm is a significantly improved version of the grammar fragment from chapter 8 of the following book: Schwarze, Christoph & Alencar, Leonel F. de. Lexikalisch-funktionale Grammatik: Eine Einführung am Beispiel des Französischen mit computerlinguistischer Implementierung [Lexical-Functional Grammar: A French-based Introduction with Computational Implementation]. Tübingen: Stauffenburg, 2016. X, 271 pp. Stauffenburg Einführungen, 30. ISBN 978-3-95809-411-6. If you publish work that uses FrGramm, please cite the following paper: Alencar, Leonel Figueiredo de (2017). A computational implementation of periphrastic verb constructions in French. Alfa, Rev. Linguíst. (São José Rio Preto), 61. To appear. http://www.scielo.br/scielo.php?script=sci_serial&pid=1981-5794&lng=en&nrm=iso

Detects Indian Sign Language gestures using ML (RF & CNN). Supports dual-hand detection, word formation, and Hindi translation. Ideal for accessibility.

This repository contains the code for an Indian Sign Language (ISL) gesture recognition project utilizing Convolutional Neural Networks (CNNs). The aim of this project is to develop a system that can accurately recognize and interpret hand gestures in ISL, which can help in improving communication for the hearing-impaired community.

A project focused on recognizing and interpreting Indian Sign Language (ISL) using computer vision and machine learning techniques. The system processes visual inputs to identify hand gestures and map them to corresponding signs, aiming to improve accessibility and communication.

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"Real-time Indian Sign Language (ISL) gesture recognition system using Mediapipe and LSTM for converting hand gestures into text, improving communication accessibility.

Communication is essential in daily life, yet people with hearing or speech impairments face difficulties when others do not understand Indian Sign Language (ISL). This system uses AI, MediaPipe, and a CNN-LSTM model to translate ISL gestures captured through a webcam into Malayalam text and speech, improving accessibility and communication.

SignSpeak is a real-time Indian Sign Language (ISL) translator that converts hand gestures into text and speech using computer vision and deep learning. Built with Python, MediaPipe, OpenCV, and TensorFlow to improve accessibility and inclusive communication.

Python project to create an enhanced Indian Sign Language (ISL) alphabet dataset. Improves generalization by adding random backgrounds and applying image processing techniques like sharpening, blurring, and edge enhancement, producing high-quality images for robust ML model training.

Emotion-Aware Speech-to-Sign Language Converter is a web-based system that translates speech into Indian Sign Language (ISL) using a 3D avatar. It uses speech recognition, NLP, and emotion detection to generate expressive sign gestures, improving communication between hearing individuals and the deaf community.

Indian Sign Language Translation using Machine Learning is a real-time system that converts ISL hand gestures into text and speech using CNNs, Mediapipe, and OpenCV. It features continuous learning, a Tkinter-based UI, and aims to improve accessibility and inclusivity for the hearing-impaired community.

The project aims to create a real-time, browser-based application that recognizes Indian Sign Language (ISL) gestures using webcam input. It translates the gestures into editable text and offers an option to download the text as a PDF. This system is intended to improve communication and accessibility for the hearing- and speech- impaired community