4,092 research outputs found
Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR
The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light
term conation step and useful in case of few language-specific resources. For English, the corpusbased
stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR.
Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from
selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness
compared to using a fixed number of terms for different languages
BdSpell: A YOLO-based Real-time Finger Spelling System for Bangla Sign Language
In the domain of Bangla Sign Language (BdSL) interpretation, prior approaches
often imposed a burden on users, requiring them to spell words without hidden
characters, which were subsequently corrected using Bangla grammar rules due to
the missing classes in BdSL36 dataset. However, this method posed a challenge
in accurately guessing the incorrect spelling of words. To address this
limitation, we propose a novel real-time finger spelling system based on the
YOLOv5 architecture. Our system employs specified rules and numerical classes
as triggers to efficiently generate hidden and compound characters, eliminating
the necessity for additional classes and significantly enhancing user
convenience. Notably, our approach achieves character spelling in an impressive
1.32 seconds with a remarkable accuracy rate of 98\%. Furthermore, our YOLOv5
model, trained on 9147 images, demonstrates an exceptional mean Average
Precision (mAP) of 96.4\%. These advancements represent a substantial
progression in augmenting BdSL interpretation, promising increased inclusivity
and accessibility for the linguistic minority. This innovative framework,
characterized by compatibility with existing YOLO versions, stands as a
transformative milestone in enhancing communication modalities and linguistic
equity within the Bangla Sign Language community
Recognition of Bangladeshi Sign Language (BdSL) Words using Deep Convolutional Neural Networks (DCNNs)
In a world where effective communication is fundamental, individuals who are Deaf and Dumb (D&D) often face unique challenges due to their primary mode of communication—sign language. Despite the interpreters' invaluable roles, their lack of availability causes communication difficulties for the D&D individuals. This study explores whether the field of Human-Computer Interaction (HCI) could be a potential solution. The primary objective is to assist D&D individuals with computer applications that could act as mediators to bridge the communication gap between them and the wider hearing population. To ensure their independent communication, we propose an automated system that could detect specific Bangla Sign Language (BdSL) words, addressing a critical gap in the sign language detection and recognition literature. Our approach leverages deep learning and transfer learning principles to convert webcam-captured hand gestures into textual representations in real-time. The model's development and assessment rest upon 992 images created by the authors, categorized into ten distinct classes representing various BdSL words. Our findings show the DenseNet201 and ResNet50-V2 models achieve promising training and testing accuracies of 99% and 93%, respectively. Doi: 10.28991/ESJ-2023-07-06-019 Full Text: PD
Onsetsu hyoki no kyotsusei ni motozuita Ajia moji nyuryoku intafesu ni kansuru kenkyu
制度:新 ; 報告番号:甲3450号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2011/10/26 ; 早大学位記番号:新577
Automated Bangla sign language translation system for alphabets by means of MobileNet
Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets
Review on Classification Methods used in Image based Sign Language Recognition System
Sign language is the way of communication among the Deaf-Dumb people by expressing signs. This paper is present review on Sign language Recognition system that aims to provide communication way for Deaf and Dumb pople. This paper describes review of Image based sign language recognition system. Signs are in the form of hand gestures and these gestures are identified from images as well as videos. Gestures are identified and classified according to features of Gesture image. Features are like shape, rotation, angle, pixels, hand movement etc. Features are finding by various Features Extraction methods and classified by various machine learning methods. Main pupose of this paper is to review on classification methods of similar systems used in Image based hand gesture recognition . This paper also describe comarison of various system on the base of classification methods and accuracy rate
Word level Bangla Sign Language Dataset for Continuous BSL Recognition
An robust sign language recognition system can greatly alleviate
communication barriers, particularly for people who struggle with verbal
communication. This is crucial for human growth and progress as it enables the
expression of thoughts, feelings, and ideas. However, sign recognition is a
complex task that faces numerous challenges such as same gesture patterns for
multiple signs, lighting, clothing, carrying conditions, and the presence of
large poses, as well as illumination discrepancies across different views.
Additionally, the absence of an extensive Bangla sign language video dataset
makes it even more challenging to operate recognition systems, particularly
when utilizing deep learning techniques. In order to address this issue,
firstly, we created a large-scale dataset called the MVBSL-W50, which comprises
50 isolated words across 13 categories. Secondly, we developed an
attention-based Bi-GRU model that captures the temporal dynamics of pose
information for individuals communicating through sign language. The proposed
model utilizes human pose information, which has shown to be successful in
analyzing sign language patterns. By focusing solely on movement information
and disregarding body appearance and environmental factors, the model is
simplified and can achieve a speedier performance. The accuracy of the model is
reported to be 85.64%
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