Search Results for "language recognition" - Page 8

Aplication for language and encoding recognition based on methods of machine learning. Command-line interface.

Project aim to provide simple easy APIs for Java developers to use interactive abilities in their Java Applications like speech recognition, handwriting recognition, use of web cam , sound record/play, decision trees , text to speech and many others.

Arabisc is speaker independent large vocabulary continuous speech recognizer for Arabic language released under GNU license.It is also a collection of open source tools that allows researchers and developers to build speech recognition systems for Arab

Simple interactive Java browser is basic simple browser show how we can use voice commands to navigate in web sites and use hyperlinks as a voice commands.

UCR is a project name for the development of an handwritten characters in Korean language. The goal is to create a UCR Library for handwriting as well as OCR from off-line, on-line data. And we have a plan to build a UCR library for mobile.

Scalable Language API (SLAPI) The most comprehensive architecture for conversational natural-language applications including speech recognition/synthesis, semantics, & machine translation. Used on Android & other mobile app platforms.

A voice recognition application which detect Quran (Islamic Holy book) pronunciation and result in a detection success percentage for each word of the Quran statements, when complete it can be considered a base for Arabic language recognition.

TBLTools is a set of GATE processing resources that implements the Fast Transformation Based Learning Algorithm. You can train it to learn rules for NLP tasks such as Named Entity Recognition and Shallow parsing.

Augmenting other natural interfaces namely handwriting, speech recognition to input Indian language characters to the computer. Speech synthesis is also provided to read out local language text.

TURing human IDentification, a textual highly accessible alternative to image CAPTCHAs involving the usage of simple phrases and based on the language recognition features of the user, supposedly human.

baobab is an implementation of FPTrees or Frequent Pattern Trees, a pattern recognition/data mining technique. it has innumerable applications in language processing, clickstream analysis, etc.

Speach recognition project for Lithuanian language.

Single webpage-based FaBBy is simple, embeddable, fully customizable PHP/MySQL-based bulletin board. Everything can be customized and it supports XHTML-templates and language files. Features include URL recognition, flood protect, badword-filter and more

Leni is an experimental human language recognition and processing tool which is based on learning and therefore nearly independent on any particular spoken language.

Unlimited-OCR is Baidu’s open-source optical character recognition (OCR) model designed to accurately extract and understand text from complex documents, images, and multilingual content. Unlike traditional OCR systems that focus only on text detection and transcription, Unlimited-OCR combines advanced document parsing with language understanding, enabling it to recognize structured elements such as tables, formulas, charts, and mixed-layout documents while preserving their logical organization. ...

Bio_ClinicalBERT is a domain-specific language model tailored for clinical natural language processing (NLP), extending BioBERT with additional training on clinical notes. It was initialized from BioBERT-Base v1.0 and further pre-trained on all clinical notes from the MIMIC-III database (~880M words), which includes ICU patient records. The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. ...

wav2vec2-large-xlsr-53-russian is a fine-tuned automatic speech recognition (ASR) model based on Facebook’s wav2vec2-large-xlsr-53 and optimized for Russian. It was trained using Mozilla’s Common Voice 6.1 and CSS10 datasets to recognize Russian speech with high accuracy. The model operates best with audio sampled at 16kHz and can transcribe Russian speech directly without a language model. It achieves a Word Error Rate (WER) of 13.3% and Character Error Rate (CER) of 2.88% on the Common Voice test set, with even better results when used with a language model. ...

wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER).