Handwritten Numeral Recognition Using Modified BP ANN Structure

Choudhary, Amit; Rishi, Rahul; Ahlawat, Savita

doi:10.1007/978-3-642-17881-8_6

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…Although in our previous work [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] we were able to achieve high recognition ratios, our mission in this research is to extend our findings and to build upon our previous results. Specifically, the objective of this work is to build a universal numeral recognizer; one that can recognize both printed and handwritten multilingual numerals.…”

Section: Background and State-of-the-art Researchmentioning

confidence: 55%

“…The achieved recognition ratios were around 98%, on average. In their work [13], Choudhary et al used a supervised learning technique based on the artificial neural network (ANN) for offline handwritten numeral recognition, where they employed a multilayered perceptron (MLP) with one hidden layer. The drawback of this work is the small-sized data set with very few samples.…”

Section: Background and State-of-the-art Researchmentioning

confidence: 99%

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Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

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Section: Background and State-of-the-art Researchmentioning

confidence: 55%

Section: Background and State-of-the-art Researchmentioning

confidence: 99%

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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“…Handwriting recognition has already achieved impressive results using shallow networks [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Many papers have been published with research detailing new techniques for the classification of handwritten numerals, characters and words.…”

Section: Related Workmentioning

confidence: 99%

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Ahlawat¹,

Choudhary²,

Nayyar

et al. 2020

Sensors

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170

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Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.

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“…Slant estimation and correction is achieved by analysis of the slanted vertical projections at various angles [2].…”

Section: Pre-processingmentioning

confidence: 99%

“…2(c). The resized characters were clubbed together in a matrix of size 48 X 26 to form a sample [2]. In the sample, each column corresponds to an English alphabet which was resized into 48 X 1 input vector.…”

Section: Binarizationmentioning

confidence: 99%

Improving the Character Recognition Efficiency of Feed Forward BP Neural Network

Choudhary¹,

Rishi²

2011

IJCSIT

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Handwritten Numeral Recognition Using Modified BP ANN Structure

Cited by 12 publications

References 6 publications

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Improving the Character Recognition Efficiency of Feed Forward BP Neural Network

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