Towards Performance Improvement in Indian Sign Language Recognition
Sign language is a complete natural language used by deaf and dumb people. It has its own grammar and it differs with spoken language to a great extent. Since people without hearing and speech impairment lack the knowledge of the sign language, the deaf and dumb people find it difficult to communicate with them. The conception of system that would be able to translate the sign language into text would facilitate understanding of sign language without human interpreter. This paper describes a systematic approach that takes Indian Sign Language (ISL) video as input and converts it into text using frame sequence generator and image augmentation techniques. By incorporating these two concepts, we have increased dataset size and reduced overfitting. It is demonstrated that using simple image manipulation techniques and batch of shifted frames of videos, performance of sign language recognition can be significantly improved. Approach described in this paper achieves 99.57% accuracy on the dynamic gesture dataset of ISL.
Code (0)
등록된 구현이 없습니다.
Tasks
Image AugmentationImage ManipulationSign Language RecognitionSimilar Papers 제목 키워드 기반
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages
Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean training instances. This paper proposes an e…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+3Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answerin…
Information RetrievalMachine TranslationMultilingual Named Entity Recognitionnamed-entity-recognition+5Bharat Scene Text: A Novel Comprehensive Dataset and Benchmark for Indian Language Scene Text Understanding
Reading scene text, that is, text appearing in images, has numerous application areas, including assistive technology, search, and e-commerce. Although scene text recognition in English has advanced significantly and is …
Scene Text RecognitionScene Text DetectionAn Overview of Indian Spoken Language Recognition from Machine Learning Perspective
Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance o…
Language IdentificationSpoken language identificationTowards Orthographically-Informed Evaluation of Speech Recognition Systems for Indian Languages
Evaluating ASR systems for Indian languages is challenging due to spelling variations, suffix splitting flexibility, and non-standard spellings in code-mixed words. Traditional Word Error Rate (WER) often presents a blea…
Speech Recognition