A CNN-LSTM based ensemble framework for in-air handwritten Assamese character recognition

Choudhury, Ananya; Sarma, Kandarpa Kumar

doi:10.1007/s11042-020-10470-y

Cited by 11 publications

(3 citation statements)

References 43 publications

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“…Several studies have examined Assamese handwriting recognition issues. An ensemble system uses deep learning models like CNNs and LSTMs to improve recognition accuracy [7]. Ensemble techniques can improve Assamese handwriting recognition and social inclusion, according to this study.…”

Section: Related Studiesmentioning

confidence: 86%

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Borah,

Baruah,

Dey

et al. 2023

IJACSA

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This study conducts a thorough assessment of ensemble machine learning methods, specifically focusing on the identification of Assamese words. This task is crucial for improving Content-Based Image Retrieval systems and safeguarding the digital heritage of Assamese culture. We analyze the efficacy of different algorithms, such as CatBoost, XGBoost, Gradient Boosting, Random Forest, Bagging, AdaBoost, Stacking, and Histogram-Based Gradient Boosting, by thoroughly examining their performance in terms of accuracy, precision, recall, Kappa, F1-score, Matthews Correlation Coefficient, and AUC. The Cat-Boost algorithm stands out as the top performer, achieving an accuracy rate of 97.7%, precision rate of 95%, and recall rate of 96%. XGBoost is also acknowledged for its substantial effectiveness. This comparative analysis emphasizes CatBoost's superiority in terms of precision and recall. Additionally, it underscores the strong ability of ensemble classifiers to enhance assistive technologies, promote social inclusivity, and seamlessly integrate the Assamese language into technological applications.

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Section: Related Studiesmentioning

confidence: 86%

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Borah,

Baruah,

Dey

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Multiple classification algorithms have tested their performance in writing recognition, including LSTM, CNN and BiLSTM. These algorithms were chosen in light of their highly accurate outcomes for text-writing recognition obtained by other researchers [19,29,30,32].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Finger-Writing Character Recognition via ToF Sensors on Edge Deep Learning

et al. 2023

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Human–computer interaction is demanded for natural and convenient approaches, in which finger-writing recognition has aroused more and more attention. In this paper, a device-free finger-writing character recognition system based on an array of time-of-flight (ToF) distance sensors is presented. The ToF sensors acquire distance values between sensors to a writing finger within a 9.5 × 15 cm square on a surface at specific time intervals and send distance data to a low-power microcontroller STM32F401, equipped with deep learning algorithms for real-time inference and recognition tasks. The proposed method enables one to distinguish 26 English lower-case letters by users writing with their fingers and does not require one to wear additional devices. All data used in this work were collected from 21 subjects (12 males and 9 females) to evaluate the proposed system in a real scenario. In this work, the performance of different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNNs) and bidirectional LSTM (BiLSTM), was evaluated. Thus, these algorithms provide high accuracy, where the best result is extracted from the LSTM, with 98.31% accuracy and 50 ms of maximum latency.

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“…An increasing number of research studies have been conducted on air quality [9]. Machine learning (ML) algorithms have been successfully applied to air-quality prediction by several researchers [10][11][12][13][14]. The support vector regression machine is a ML method used to minimize structural risk based on statistical learning theory [15].…”

Section: Introductionmentioning

confidence: 99%

Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Yuan

Dai

et al. 2022

Sustainability

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Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and health of China’s people. However, the diversity and complexity of the factors which affect air pollution render air quality monitoring data complex and nonlinear. To improve the accuracy of prediction of the air quality index (AQI) and obtain more accurate AQI data with respect to their nonlinear and nonsmooth characteristics, this study introduces an air quality prediction model based on the empirical mode decomposition (EMD) of LSTM and uses improved particle swarm optimization (IPSO) to identify the optimal LSTM parameters. First, the model performed the EMD decomposition of air quality data and obtained uncoupled intrinsic mode function (IMF) components after removing noisy data. Second, we built an EMD–IPSO–LSTM air quality prediction model for each IMF component and extracted prediction values. Third, the results of validation analyses of the algorithm showed that compared with LSTM and EMD–LSTM, the improved model had higher prediction accuracy and improved the model fitting effect, which provided theoretical and technical support for the prediction and management of air pollution.

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A CNN-LSTM based ensemble framework for in-air handwritten Assamese character recognition

Cited by 11 publications

References 43 publications

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Real-Time Finger-Writing Character Recognition via ToF Sensors on Edge Deep Learning

Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

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