Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Ahlawat, Savita; Choudhary, Amit; Nayyar, Anand; Singh, Saurabh; Yoon, Byungun

doi:10.3390/s20123344

Cited by 205 publications

(82 citation statements)

References 64 publications

(67 reference statements)

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“…To illustrate the effectiveness and the robustness of our proposed algorithm, different noisy environments are also considered. We select the updated state-of-the-art algorithm, which is IHRS-CNN [ 29 ]. The implementation of IHRS-CNN algorithm is also straightforward.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, the authors generated their own samples, which are given by 6000 handwritten Devanagari digit. Recently, an improved handwritten recognition method using a CNN (IHRS-CNN) method has been proposed in [ 29 ] for improving the performance of handwritten digit recognition while maintaining the computational complexity. In particular, the IHRS-CNN method makes use of a pure CNN architecture only without the requirement of using any ensemble architecture, which could increase both the cost and computational complexity.…”

Section: Related Workmentioning

confidence: 99%

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A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Abdulhussain

Mahmmod

Naser

et al. 2021

Sensors

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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Abdulhussain

Mahmmod

Naser

et al. 2021

Sensors

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“…These datasets provide more than 60,000 images, and represent the gold standard for comparison. A lot of deep learning methods [ 20 , 21 ] have been compared based on many kinds of benchmarks [ 18 , 19 , 22 , 23 ]; for example, handwritten digits [ 24 ] and lung nodule detection [ 25 ]. However, these cases were not applied in this study.…”

Section: Discussionmentioning

confidence: 99%

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

Cheng

Chi

Yang

et al. 2020

Sensors

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In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

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“…Image classification using CNNs has been used to identify whether a given radiology image indicates the presence or absence of a certain disease [12]. Image classification CNNs are also used to identify handwritten digits [13]. Facial recognition, which is assigning a name to a face in an image, is a classification task [14].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Convolutional Neural Network Computer Vision System to Prevent Sudden Infant Death Syndrome

Bharati¹

2020

Preprint

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Sudden Infant Death Syndrome (SIDS) causes infants under one year of age to die inexplicably. One of the most important external factors responsible for the syndrome, called an ‘outside stressor’, is the sleeping position of the baby. When the baby sleeps on the stomach with face down, the risk of SIDS occurring is very high. We propose a Convolutional Neural Network (CNN) based computer vision system that can alert caregivers on their mobile phones within a few seconds of the baby moving to a hazardous face-down sleeping position. The model processes real-time image feeds with a single efficient forward pass. It has a low computational load and a low memory footprint. This would allow it to be embedded in low power edge devices such as crib cameras. Processing at the edge would also alleviate privacy concerns in sending images into the network. The CNN architecture is composed of multiple sets of processing units, each unit containing a 2D convolutional layer with the Rectified Linear Unit activation function followed by a Max Pooling layer. The final layer in the architecture is a fully connected dense layer with the Sigmoid activation function and outputs three classes of sleeping position indicators. We experimented with various numbers of convolutional processing units and dense layers as well as the number of convolutional kernels to arrive at the optimal production configuration. We observed a consistently high accuracy of detection of sleeping position changes to turning and face-down positions with a trend towards even higher accuracies with caregiver feedback. Therefore, this system is a viable candidate for consideration as a non-intrusive technology to assist in preventing the Sudden Infant Death Syndrome.

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Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Cited by 205 publications

References 64 publications

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

An Efficient Convolutional Neural Network Computer Vision System to Prevent Sudden Infant Death Syndrome

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