Alphabet Recognition Research Articles

Real-time mobile application for Arabic sign alphabet recognition using pre-trained CNN

Real-time mobile application for Arabic sign alphabet recognition using pre-trained CNN

Kurdish Sign Language Recognition Using Convolutional Neural Network (CNN)

The present study examines the obstacles encountered by the deaf population, with a specific emphasis on the increasing significance of sign language in facilitating effective communication. The main mode of communication for deaf persons is Sign Language (SL), which is a visual and expressive method of communicating meaning via facial expressions, hand motions, and body gestures. The objective of this project is to automate the identification of sign language in order to improve accessibility and decrease reliance on interpreters. Specifically, the work focused on constructing an alphabet recognition system for Kurdish Sign Language (KSL). Due to its many intricacies and resemblances to the Arabic script, KSL requires a strong recognition model. The proposed method utilizes Convolutional Neural Networks (CNN) with an actual dataset to accurately recognize both numerical values and alphabetic characters in the Korean Sign Language (KSL). Real-time operation allows for rapid recognition of hand gestures and subsequent provision of textual output. The collection comprises 132,000 hand pictures, including 33 alphabetica and (0-9) numeral signs. Significantly, the use of MediaPipe, a method for processing 3D images, greatly improves the efficiency of detection. Multiple methodologies were investigated, and the integration of Convolutional Neural Networks (CNN), TensorFlow, and MediaPipe resulted in a remarkable accuracy of 99.87% with negligible dropout rates. This study establishes a foundation for enhanced communication and independence for the deaf population, representing a notable advancement in the automation of sign language recognition.

Application-Based Assembled Edu Media to Stimulate Early Childhood Alphabet Recognition Abilities

Application-Based Assembled Edu Media to Stimulate Early Childhood Alphabet Recognition Abilities

Sign Language Detection and Recognition Using Media Pipe and Deep Learning Algorithm

People lacking the sense of hearing and the ability to speak have undeniable communication problems in their life. People with hearing and speech problems communicate using sign language with themselves and others. These communicating signs are made up of the shape of the hand and movement. Sign language is not essentially known to a more significant portion of the human population who uses spoken and written language for communication. Therefore, it is a necessity to develop technological tools for interpretation of sign language. Much research have been carried out to acknowledge sign language using technology for most global languages. But there are still scopes of development of tools and techniques for sign language development for local dialects. This work attempts to develop a technical approach for recognizing American Sign Language Using machine learning techniques, this work tried to establish a system for identifying the hand gestures from American Sign Language. A combination of two-dimensional and three-dimensional images of Assamese gestures has been used to prepare a dataset. The Media Pipe framework has been implemented to detect landmarks in the images. The results reveal that the method implemented in this work is effective for the recognition of the other alphabets and gestures in Sign Language. This method could also be tried and tested for the recognition of signs and gestures for various other local languages of India

Korean Sign Language Alphabet Recognition Through the Integration of Handcrafted and Deep Learning-Based Two-Stream Feature Extraction Approach

Korean Sign Language Alphabet Recognition Through the Integration of Handcrafted and Deep Learning-Based Two-Stream Feature Extraction Approach

Data Testing in Digital Transmission: Tamil Literature

The developing area of Optical Character Recognition (OCR) is digital handwriting recognition. Manual writing is replaced by a digital writing pad. The font and shape of the letters vary while writing digitally. The writer’s digital pen pressure and position on the digital pad cause covert text file problems during OCR recognition. When converting OCR to text, an error occurs because of the variations in letter shapes. In languages like Tamil, Chinese, Arabic, and Telugu, where the alphabet is made up of bends, curves, and rings, the aforementioned issue occurs. Tamil has more word mistakes in OCR-to-text conversion because the alphabet comprises curves and angles that must be correctly transcribed. The ResNet (Residual Neural Network) Two-Stage Bottleneck Architecture (RTSBA) is suggested in this paper. In order to recognize text written in Tamil on a digital writing pad, this article suggests using ResNet (Residual Neural Network) Two-Stage Bottleneck Architecture (RTSBA). The suggested RTSBA reduces the complexity of the Tamil alphabet recognition problem by using two distinct phases of neural networks. There are fewer inputs and variables in the early stages. Time and computational complexity are minimized in the last phase. A two-channel and two-stream transformer, long short-term memory, Inception-v3, recurrent neural networks, convolutional neural networks, and other conventional algorithms have been compared to the suggested algorithm. The digital writing pad-handwritten and HP lab datasets demonstrate the effectiveness of proposed methods like RTSBA, which yield accuracy rates of 98.7% and 97.1%, respectively.

Handwritten alphabet classification in Tamil language using convolution neural network

Handwritten Alphabet Recognition can be defined as the way of detecting characters from images of Handwritten language alphabets. This is one of the important problems that can be solved by Convolution Neural Networks (CNN). Recent developments in CNN have made it possible to expand this problem area from English character recognition or Numbers recognition to Regional Languages character recognition, there has not been sufficient studies conducted in the domain of regional languages. This study has attempted to give deep learning approach to Tamil Handwritten Alphabets classification. This article aims to develop 3 models of CNN – THAC-CNN1, THAC-CNN2 and THAC-CNN3 to recognize Tamil Handwritten Alphabets and classify them based on its category. Our proposed models use a combination of benchmark dataset and a customized dataset which totals to over 2800 images of different Tamil alphabets after various data augmentation techniques. The proposed models are compared with a popular image classification pre-trained models - VGG-11 and VGG-16. We use the standard classification metric - accuracy to measure the performance of our proposed models. With our dataset and augmentation techniques, one of our models THAC-CNN1 achieves 97% accuracy on the training dataset and 92.5% accuracy on test dataset as opposed to 72% and 73.5% accuracy on training dataset and test dataset by pre-trained models.

Media Big Book Alphabet Recognition in Early Childhood

Students at kindergarten can learn the alphabet through a Big Book-based lesson. It helps them master the alphabet. The goals of this research are to (1) determine the extent to which Big Book media are being used to teach the alphabet in kindergarten, (2) examine the ways in which teachers are using Big Book media to teach the alphabet, and (3) draw conclusions about the implications of utilising Big Book media to teach students to recognise individual letters. Because kindergarteners often struggle to recognise and recall the alphabet, teachers often resort to Big Book media. To aid children's memorisation and speed with letter recognition, the Big Book includes a section devoted to the alphabet. Qualitative methods of description were used in this investigation. Observation, interviews, and thorough documentation are the backbone of the data collection process. The three phases of data analysis are reducing data, presenting data, and making conclusions. The study's findings suggest that (1) Big Book media improves children's letter recognition when compared to other media. (2) In this kindergarten, teachers use Big Book media for teaching letter recognition since it is more engaging and straightforward for young students. Three implications of Big Book media, which is used as a medium to teach kids the alphabet in kindergarten, based on markers of progress towards quicker and easier letter recognition.

Wearable sensors for real-time physiological monitoring based on self-assembled diphenylalanine peptide nanostructures

Self-assembled diphenylalanine short peptides show great potential in wearable and flexible biomedical devices due to its excellent biocompatibility and piezoelectric nature. Herein, the piezoelectric activity of self-assembled diphenylalanine analogues by rationally selecting chemical linkers such as ethylene diamine, succinic acid and terephthalaldehyde. Our results show that the self-assembled diphenylalanine with terepthalaldehyde linker has a favorable effect on the piezoelectric activity. The output of an optimized SP3 PNG can reach 29 V and 480 nA, and, it has exhibited excellent stability, repeatability, and charging-discharging capacity. Such a device can be used to harness energy or detect biomechanical actions such as swallowing, finger bending, coughing, posture angle monitoring and alphabet recognition. The PNG has been used to monitor arterial pulses as a biomedical application model. The obtained results are steppingstones towards developing robust bio-implantable PNGs for powering pacemakers and implantable cardioverter-defibrillators (ICDs) in future.

Convolution neural networks with hybrid feature extraction methods for classification of voice sound signals

The convolutional neural networks (CNNs) lead in the domain of Sound Recognition due to its flexibility and ability with different adjusting parameters. The recognition of spoken English Alphabets by different people with deep learning techniques attracted the research community. In this paper, we are exploring the use of convolutional neural network (CNN), a deep learner that can automatically learn features directly from the dataset while training for the classification of sounds signals of English alphabets. In this proposed work, we consider two CNN architectures. In first architecture, we propose MFCC based features for pretrained two convolutional layer CNN architecture. In the second architecture, we propose a hybrid feature extraction method to train a block-based CNN architecture. The proposed systems consist of two components namely hybrid feature extraction and CNN classifier. The five auditory features log-Mel spectrogram (LM), MFCC, chroma, spectral contrast and Tonnetz features are extracted and then LM & MFCC are combined as one feature set. LM, MFCC, and CST features are aggregated as another for training to the proposed two CNNs, respectively. The different sound samples of English alphabets are collected from different people of different age groups. The feature sets collected from the hybrid feature extraction methods are presented to both the proposed CNNs and the experimental results are collected. The experimental results indicate that the taxonomic accuracy of the proposed architectures can surpass the existing methods of CNNs with single feature extraction methods. The proposed second architecture performs more effectively over the proposed first CNN architecture.

Development and Validation of Handwriting Assessment (OTULIS)

ABSTRACT Handwriting is fundamental to classroom learning, such as taking notes, doing homework, and taking examinations. Children with handwriting problems are often left behind and have difficulty engaging in classroom learning. To date, no handwriting assessment has been developed in Bahasa Malaysia specifically for the Malaysian population. Therefore, this study aimed to develop and validate a handwriting assessment tool (OTulis) for children aged 4–6 in Malaysia. This study used qualitative and quantitative approaches and was divided into three phases: (i) the development of OTulis, (ii) content validation, and (iii) cognitive interviews. The respondents were purposively recruited in all phases. Phase 1 was analyzed using thematic analysis. Phase 2 was analyzed using thematic analysis, content validity index (CVI), and modified kappa (k*). Phase 3 was analyzed using content analysis. The findings demonstrated that the OTulis assessment comprises pencil grip, posture, fine motor control and precision, visual memory, copying shapes, alphabet recognition, and copying sentences. Furthermore, OTulis achieved an I-CVI of 0.88–1.00, S-CVI/Ave of 0.99–1.00, and S-CVI/UA of 0.90–1.00. In addition, OTulis achieved excellent inter-rater agreement with modified kappa scores of 0.88–1.00. Therefore, the OTulis is a valid handwriting assessment tool for the Malaysian population aged 4–6.

AttentionMNIST: a mouse-click attention tracking dataset for handwritten numeral and alphabet recognition

Multiple attention-based models that recognize objects via a sequence of glimpses have reported results on handwritten numeral recognition. However, no attention-tracking data for handwritten numeral or alphabet recognition is available. Availability of such data would allow attention-based models to be evaluated in comparison to human performance. We collect mouse-click attention tracking data from 382 participants trying to recognize handwritten numerals and alphabets (upper and lowercase) from images via sequential sampling. Images from benchmark datasets are presented as stimuli. The collected dataset, called AttentionMNIST, consists of a sequence of sample (mouse click) locations, predicted class label(s) at each sampling, and the duration of each sampling. On average, our participants observe only 12.8% of an image for recognition. We propose a baseline model to predict the location and the class(es) a participant will select at the next sampling. When exposed to the same stimuli and experimental conditions as our participants, a highly-cited attention-based reinforcement model falls short of human efficiency.

2D Camera-Based Air-Writing Recognition Using Hand Pose Estimation and Hybrid Deep Learning Model

Air-writing is a modern human–computer interaction technology that allows participants to write words or letters with finger or hand movements in free space in a simple and intuitive manner. Air-writing recognition is a particular case of gesture recognition in which gestures can be matched to write characters and digits in the air. Air-written characters show extensive variations depending on the various writing styles of participants and their speed of articulation, which presents quite a difficult task for effective character recognition. In order to solve these difficulties, this current work proposes an air-writing system using a web camera. The proposed system consists of two parts: alphabetic recognition and digit recognition. In order to assess our proposed system, two character datasets were used: an alphabetic dataset and a numeric dataset. We collected samples from 17 participants and asked each participant to write alphabetic characters (A to Z) and numeric digits (0 to 9) about 5–10 times. At the same time, we recorded the position of the fingertips using MediaPipe. As a result, we collected 3166 samples for the alphabetic dataset and 1212 samples for the digit dataset. First, we preprocessed the dataset and then created two datasets: image data and padding sequential data. The image data were fed into the convolution neural networks (CNN) model, whereas the sequential data were fed into bidirectional long short-term memory (BiLSTM). After that, we combined these two models and trained again with 5-fold cross-validation in order to increase the character recognition accuracy. In this work, this combined model is referred to as a hybrid deep learning model. Finally, the experimental results showed that our proposed system achieved an alphabet recognition accuracy of 99.3% and a digit recognition accuracy of 99.5%. We also validated our proposed system using another publicly available 6DMG dataset. Our proposed system provided better recognition accuracy compared to the existing system.

Real-time Assamese Sign Language Recognition using MediaPipe and Deep Learning

People lacking the sense of hearing and the ability to speak have undeniable communication problems in their life. People with hearing and speech problems communicate using sign language with themselves and others. Sign language is not essentially known to a more significant portion of the human population who uses spoken and written language for communication. Therefore, it is a necessity to develop technological tools for interpretation of sign language. Much research have been carried out to acknowledge sign language using technology for most global languages. But there are still scopes of development of tools and techniques for sign language development for local dialects. There are 22 modern Indian languages and more than 19000 languages that are spoken regionally as mother tongue. This work attempts to develop a technical approach for recognizing Assamese Sign Language, which is one of the 22 modern languages of India. Using machine learning techniques, this work tried to establish a system for identifying the hand gestures from Assamese Sign Language. A combination of two-dimensional and three-dimensional images of Assamese gestures has been used to prepare a dataset. The MediaPipe framework has been implemented to detect landmarks in the images. An Assamese Sign Language dataset of 2094 data points has been generated, including nine static gestures with vowels and consonants (অ, ই, ঈ, উ, এ, ও, ক, জ, ল) from the Assamese Sign Language. The dataset was used for training of a feed-forward neural network. The model yielded an accuracy of 99%. The results reveal that the method implemented in this work is effective for the recognition of the other alphabets and gestures in the Assamese Sign Language. This method could also be tried and tested for the recognition of signs and gestures for various other local languages of India.

Tamil OCR Conversion from Digital Writing Pad Recognition Accuracy Improves through Modified Deep Learning Architectures

Digital handwritten recognition is an emerging field in optical character recognition (OCR). A digital writing pad replaces manual writing. In digital writing, the alphabet changes in font and shape. During OCR recognition, covert text file errors occur due to digital pen pressure and digital pen position on the digital pad by the writer. The shape changes in the alphabet lead to an error during the conversion of OCR to text. The above problem arises in Tamil, Chinese, Arabic, and Telugu, where the alphabet consists of bends, curves, and rings. OCR‐to‐text conversion for the Tamil language has more word errors due to angles and curves in the alphabet, which need to be converted accurately. This paper proposes ResNet two‐stage bottleneck architecture (RTSBA) for Tamil language‐based text recognition written on a digital writing pad. In the proposed RTSBA, two separate stages of neural networks reduce the complexity of the Tamil alphabet recognition problem. In the initial stage, the number of inputs and variables is reduced. In the final stage, time and computation complexity are reduced. The proposed algorithm has been compared with traditional algorithms such as long short‐term memory, Inception‐v3, recurrent neural networks, convolutional neural networks, and a two‐channel and two‐stream transformer. Proposed methods, such as RTSBA applied in the digital writing pad‐handwritten and HP lab datasets, achieved an accuracy of 98.7% and 97.1%, respectively.

Recognition of Handwritten Words from Digital Writing Pad Using MMU-SNet

In this paper, Modified Multi-scale Segmentation Network (MMU-SNet) method is proposed for Tamil text recognition. Handwritten texts from digital writing pad notes are used for text recognition. Handwritten words recognition for texts written from digital writing pad through text file conversion are challenging due to stylus pressure, writing on glass frictionless surfaces, and being less skilled in short writing, alphabet size, style, carved symbols, and orientation angle variations. Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines, angles, curves, and bends. The small change in dots, curves, and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet conversion. However, handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine (SVM), Kohonen Neural Network (KNN), and Convolutional Neural Network (CNN) for offline and online alphabet recognition. The proposed algorithms are compared with above algorithms for Tamil word recognition. The proposed MMU-SNet method has achieved good accuracy in predicting text, about 96.8% compared to other traditional CNN algorithms.

American Sign Language Alphabet Recognition Using Inertial Motion Capture System with Deep Learning

Sign language is designed as a natural communication method for the deaf community to convey messages and connect with society. In American sign language, twenty-six special sign gestures from the alphabet are used for the fingerspelling of proper words. The purpose of this research is to classify the hand gestures in the alphabet and recognize a sequence of gestures in the fingerspelling using an inertial hand motion capture system. In this work, time and time-frequency domain features and angle-based features are extracted from the raw data for classification with convolutional neural network-based classifiers. In fingerspelling recognition, we explore two kinds of models: connectionist temporal classification and encoder-decoder structured sequence recognition model. The study reveals that the classification model achieves an average accuracy of 74.8% for dynamic ASL gestures considering user independence. Moreover, the proposed two sequence recognition models achieve 55.1%, 93.4% accuracy in word-level evaluation, and 86.5%, 97.9% in the letter-level evaluation of fingerspelling. The proposed method has the potential to recognize more hand gestures of sign language with highly reliable inertial data from the device.

Teach- Nology: A tool for improving phonemic awareness of non- graded learners with special education needs (LSENs)

This study aimed to determine the effects Teach-Nology as a tool for improving phonemic awareness of non- graded Learners with Special Education Needs (LSENs). Parameters were based on pretest and posttest. The researchers used the experimental descriptive design thus, applying different statistical treatment such as mean, standard deviation and dependent t- test. Overall mean results for pretest and posttest for the phonemic awareness were 4.20 and 7.00 having Fairly Satisfactory and Very Satisfactory verbal interpretation. The overall mean result in the posttest was greater compared to pretest, this implied that there was an increase in the alphabet recognition of the subjects before and after the exposure to Teach-Nology. Also, the dependent t-test results revealed that the level of performance in alphabet recognition of respondents after the exposure to Teach-Nology differ significantly since the p-value is less than 0.05. With such results, the null hypothesis is rejected with verbal interpretation of significant. In conclusion, Teach-Nology helps in improving the level of performance in phonemic awareness of students, specifically the non-graded learners with special educational needs of Guronasyon Foundation Inc. National High School. However, continuous effort and emphasis on enhancing the level of performance in alphabet recognition should be given to improve their skill.

DEAF-BSL: Deep lEArning Framework for British Sign Language recognition

The recent development of disability studies in academic bodies has expedited the promotion of investigation on disability. With computer-aided tools, communication between the impaired person and someone who does not understand sign language could be accessible. A large number of people across the world are using sign language (e.g., British Sign Language (BSL) , Asian Sign Language (ASL) , Indian Sign Language (ISL) , etc.) with hand gestures for communication. In BSL recognition, the involvement of both hands overlapping each other becomes the main challenge. Moreover, BSL comprises ambiguous signs concerning viewpoint. However, existing traditional techniques seem in-stable, less accurate, and inefficient. In this work, the BSL fingerspelling alphabet recognition problem explores using a Deep learning framework to address the above-mentioned concerns. Convolutional Neural Network (CNN) is employed to detect and recognize for classification of 26 alphabets after being trained on the BSL corpus dataset. The proposed work outperforms the existing works with better precision (6%), recall (4%), and F-measure (5̃%). It reported better results on the BSL corpus dataset and webcam videos. The model achieved better accuracy (98.0%) for a large lexicon of words than previous models (Goh & Holden [ 6 ]: 69.5%, Rambhau [ 9 ]: 79.2%, and Liwicki et al. [ 8 ]: 92.5%). The 3D CNN-based proposal performs robust hand detection, much more accurate sign recognition, more scalability, and less ambiguity in BSL finger-spelling recognition.

Literacy learning in infant-toddler programs: Practice architectures as a lens for examining educator pedagogy

The ‘schoolification’ of early childhood education and care programs, seen as readying children for formalised schooling, has had an impact on the education of younger and younger age groups. While the focus of past research has mainly focused on 4-5-year-old children, this study shifts the focus to two-three-year-old children and the literacy focus of educators working with these very young children. Nine educators from Victoria, Australia were interviewed, asked to share their views on literacy learning and development for two-three-year-old children and the planning, assessment and practices they engage in to support children’s literacy development. Responses were analysed using a practice architectures lens. Findings showed that play-based pedagogies were often overlooked when seeking to support children’s literacy development, with a preference for more formalised foci on print-based learning activities, including a focus on alphabetic letter recognition. Implications for children’s literacy learning and practice are also discussed.