An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

Ali, Saqib; Shaukat, Zeeshan; Azeem, Muhammad; Sakhawat, Zareen; Mahmood, Tariq; Rehman, Khalil Ur

doi:10.1007/s42452-019-1161-5

Cited by 72 publications

(31 citation statements)

References 44 publications

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“…They found the 95.7% test accuracy within 500 epochs from their proposed CNN model. The combined architecture of CNN along with Deeplearning4j (DL4J) was presented for MNIST dataset recognition by authors in [4]. They avoided any kind of pre-processing steps and obtained 99.21% accuracy from their model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recognition or classification process is done from text images and, thus, is known as optical character recognition [4]. This plays a crucial role in many applications, especially for commercial purposes.…”

Section: Introductionmentioning

confidence: 99%

“…This plays a crucial role in many applications, especially for commercial purposes. Various datasets are used for recognition in this research paradigm [4]. Different authors have used different methods to classify various digits and characters.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient CNN Model for Automated Digital Handwritten Digit Classification

Biswas

Islam

2021

JISEBI

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Background: Handwriting recognition becomes an appreciable research area because of its important practical applications, but varieties of writing patterns make automatic classification a challenging task. Classifying handwritten digits with a higher accuracy is needed to improve the limitations from past research, which mostly used deep learning approaches.Objective: Two most noteworthy limitations are low accuracy and slow computational speed. The current study is to model a Convolutional Neural Network (CNN), which is simple yet more accurate in classifying English handwritten digits for different datasets. Novelty of this paper is to explore an efficient CNN architecture that can classify digits of different datasets accurately.Methods: The author proposed five different CNN architectures for training and validation tasks with two datasets. Dataset-1 consists of 12,000 MNIST data and Dataset-2 consists of 29,400-digit data of Kaggle. The proposed CNN models extract the features first and then performs the classification tasks. For the performance optimization, the models utilized stochastic gradient descent with momentum optimizer.Results: Among the five models, one was found to be the best performer, with 99.53% and 98.93% of validation accuracy for Dataset-1 and Dataset-2 respectively. Compared to Adam and RMSProp optimizers, stochastic gradient descent with momentum yielded the highest accuracy.Conclusion: The proposed best CNN model has the simplest architecture. It provides a higher accuracy for different datasets and takes less computational time. The validation accuracy of the proposed model is also higher than those of in past works.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient CNN Model for Automated Digital Handwritten Digit Classification

Biswas

Islam

2021

JISEBI

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“…Narrowing down the problem to only number digitization, there has been extensive research in handwritten digit recognition (Ali et al, 2019) as well as credit card, and streetview imagery (Leelasantiham, 2009;Goodfellow et al, 2014). Although a variety of classifiers have been used for this purpose, such as support vector machine, k-nearest neighbors and neural networks, convolutional neural networks appear to provide the best performance for digit recognition (Ali et al, 2019). In particular, Král andČochner digitized analog gas meter readings using meter localization, perspective correction, and a digit-bydigit recognition using Linear Support Vector classification and template matching methods (Král andČochner, 2015).…”

Section: Background On Number Digitizationmentioning

confidence: 99%

CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera

Kulkarni

Katebi

Valderrama

et al. 2021

Front. Artif. Intell.

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Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34–203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network.

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“…Deep learning is an artificial intelligence branch of machine learning that uses neural networks to learn unsupervised from unstructured or unlabeled data. Deep neural learning, or deep neural network, is another name for it (Ali et al 2019). Deep learning is an artificial intelligence function that imitates the processing of data that happens in the human brain to recognise expression, identify objects, make decisions and translate languages.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

al.¹

2021

TURCOMAT

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Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).

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An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

Cited by 72 publications

References 44 publications

An Efficient CNN Model for Automated Digital Handwritten Digit Classification

An Efficient CNN Model for Automated Digital Handwritten Digit Classification

CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera

Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

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