Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech

Ahmed, Shahjalal; Islam, Md. Rafiqul; Hassan, Jahid; Ahmed, Minhaz Uddin; Ferdosi, Bilkis Jamal; Saha, Sudipta; Shopon,

doi:10.48550/arxiv.1901.05613

Cited by 3 publications

(3 citation statements)

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“…The model is 91.1% accurate in classifying Bengali Sign images. Ahmed et al (2019) proposed a complete computer vision-based system that can recognize Bangla Sign digits and translate them to Bangla speech. Deep CNN was chosen for the classification purpose.…”

Section: Bangla Sign Language (Bsl)mentioning

confidence: 99%

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Das

Imtiaz²,

Neom³

et al. 2023

Expert Systems with Applications

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Section: Bangla Sign Language (Bsl)mentioning

confidence: 99%

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Das

Imtiaz²,

Neom³

et al. 2023

Expert Systems with Applications

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“…A structure was created that by its own identifies numbers based on hand signs and conveys the outcome in Bangla lan-guage, wherein the hand signal is identified by the proposed srtucture, generates some digits and conveys them in a line [9]. They experimented with numerous hyper-parameter combinations to obtain the best outcomes in the shortest amount of time with CNN.…”

Section: Chapter 2 Related Workmentioning

confidence: 99%

Computer Vision-Based Bengali Sign Language To Text Generation

Tazalli

Aunshu

Liya

et al. 2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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In the whole world, around 7% of people have hearing and speech impairment problems. They use sign language as their communication method. People from various countries use a variety of sign languages. As an example, there are ASL, CSL, JSL, etc. Even in our country, there are lots of people born with hearing and speech impairment problems. So, our primary focus is to work for those people by converting Bangla sign language into text. There are already various projects on Bangla sign language done by other people. However, they focused more on the separate alphabets and numerical numbers. That is why we want to concentrate on Bangla word signs since these people prefer to communicate using words or phrases rather than alphabets. There is not any proper database for Bangla word sign language, so we are making a database for Bangla word sign language for our work. In recognition of sign language (SLR), there usually are two types of scenarios: isolated SLR, which takes words by word and completes recognize action, and the other one is continuous SLR, which completes action by translating the whole sentence at once. We are working on isolated SLR. We introduce a method where we are going to use PyTorch and YOLOv5 for a video classification model to convert Bangla sign language into the text from the video where each video has only one sign language word. Here,we have achieved an accuracy rate of 76.29% on the training dataset and 51.44% on the testing dataset. We are working to build a system that will make it easier for hearing and speech-disabled people to interact with the general public.

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“…In the context of Bengali natural language processing (NLP), several approaches including deep learning (DL) techniques have proved their capabilities in several fields, such as handwritten Bengali text detection [26][27][28][29][30], using Deep learning Identification of Bangla overlaid text on videos [31][32][33], hand sign language to text using Neural Network [34], Automatic Speech Recognition [35][36][37][38], Machine Translation [39][40][41], Text Categorization [42][43][44] and Bangla Emotion Detection [44][45][46][47]. These are some works relevant to our methodology to propose a novel study on Bangladesh Bkash financial transaction awareness by detecting fraudulent texts using ML and DL.…”

Section: Introductionmentioning

confidence: 99%

Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh

Khushbu,

Jaigirdar,

Anwar

et al. 2024

Preprint

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The growth of mobile financial services over the last decade is well accepted by the people of Bangladesh. However, although reported here and there, a complete understanding of illegal payments and fraudulent transactions is still missing. This paper studies one of the popular and experienced financial transaction services of Bangladesh, Bkash, to identify fraudulent text messages through the service. We propose an improved classification method for fraudulent messages through feature extraction and a hybrid deep learning (DL) approach with ML, which combines a convolutional neural network with sequential learning, referred to as the customized Bkash fraud semantic sequential learning (CBF-SSL) model. Seven machine-learning classifiers and five deep-learning sequential models are experimentally evaluated within the hybrid classifier. The performance of the fraud detection system is assessed using the loss function and confusion matrix on a dataset of 500 Bengali text messages. The performance evaluation demonstrates that the hybrid model of convolution neural network (CNN) using long short-term memory (LSTM) outperforms all other classifiers with the lowest error 1 rate of 0.2532%, the highest F1-measure of 75.00% with the highest test accuracy. The proposed text features in combination with CNN and LSTM prove to be highly effective in detecting fraudulent messages.

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Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech

Cited by 3 publications

References 0 publications

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Computer Vision-Based Bengali Sign Language To Text Generation

Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh

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