Handwritten digit recognition through wavelet decomposition and wavelet packet decomposition

Akhtar, Muhammad Suhail; Qureshi, Hammad

doi:10.1109/icdim.2013.6693992

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…The authors have examined the effect on classification tasks using 3 different wavelet basis functions. Another work on handwritten digit recognition using SVM and KNN classifiers in [1] is powered by the wavelet.…”

Section: Related Workmentioning

confidence: 99%

Wavelet based edge feature enhancement for convolutional neural networks

Silva

Fernando

Piyatilake

et al. 2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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Convolutional neural networks are able to perform a hierarchical learning process starting with local features. However, a limited attention is paid to enhancing such elementary level features like edges. We propose and evaluate two wavelet-based edge feature enhancement methods to preprocess the input images to convolutional neural networks. The first method develops feature enhanced representations by decomposing the input images using wavelet transform and limited reconstructing subsequently. The second method develops such feature enhanced inputs to the network using local modulus maxima of wavelet coefficients. For each method, we have developed a new preprocessing layer by implementing each purposed method and have appended to the network architecture. Our empirical evaluations demonstrate that the proposed methods are outperforming the baselines and previously published work with significant accuracy gains.

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Section: Related Workmentioning

confidence: 99%

Wavelet based edge feature enhancement for convolutional neural networks

Silva

Fernando

Piyatilake

et al. 2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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“…They reported recognition accuracy of 98% on the Indian digit database provided by CENPARMI [9]. In [10] wavelet analysis based technique for feature extraction was reported. For classification, SVM and K-Nearest Neighbor (KNN) were used.…”

Section: Related Researchmentioning

confidence: 99%

Handwritten Digit Recognition Using DCT and HMMs

Ali¹,

Ghani²

2014

2014 12th International Conference on Frontiers of Information Technology

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Handwritten digits recognition has been an interesting area due to its applications in several fields. Recognition of bank account numbers and zip codes are a few examples. Handwritten digits recognition is not a trivial task due to presence of large variation in writing style in available data. In order to cope with this problem both features and classifier need to be efficient. In this research, transformation based features, Discrete Cosine Transform (2D-DCT), have been used. Hidden Markov models (HMMs) have been applied as classifier. The proposed algorithm has been trained and tested on Mixed National Institute of Standards and Technology (MNIST) handwritten digits database. The algorithm provides promising recognition results on MNIST database of handwritten digits.12th International Conference on Frontiers of Information Technology 978-1-4799-7505-1/14 $31.00

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“…The method of handwritten digit recognition both recognizes and classifies handwritten digits from (0-9) without there being any human interaction at all. It is a computer"s ability to first receive and then interpret this handwritten input intelligently from a number of sources, including photographs, paper documents, touch screens and a number of other similar devices [1]. In fact, recognizing the handwritten digits of people has been an important field of progressive research for the last few decades and studied intensely as a challenging problem [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

“…It is a computer"s ability to first receive and then interpret this handwritten input intelligently from a number of sources, including photographs, paper documents, touch screens and a number of other similar devices [1]. In fact, recognizing the handwritten digits of people has been an important field of progressive research for the last few decades and studied intensely as a challenging problem [1]- [3]. Researchers have achieved a number of results by using different algorithms, such as K-Nearest-Neighbors (KNNs), Neural Networks (NNs), etc.…”

Section: Introductionmentioning

confidence: 99%

“…Handwritten digit recognition remains a vital area because of its huge practical applications, as well as the important financial implications [1] [2]. It is promising in a wide range of application domains, including online handwriting recognition on computer tablets; zip code recognition to help sort posted mail, as well as the verification of signatures on cheques in order to thwart any attempts at bank fraud, etc.…”

Section: Introductionmentioning

confidence: 99%

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Handwritten Digit Recognition based on Output-Independent Multi-Layer Perceptrons

Keshta¹

2017

ijacsa

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With handwritten digit recognition being an established and significant problem that is facing computer vision and pattern recognition, there has been a great deal of research work that has been undertaken in this area. It is not a trivial task because of the big variation that exists in the writing styles that have been found in the available data. Therefore both, the features and the classifier need to be efficient. The core contribution of this research is the development of a new classification technique that is based on the MLP, which can be identified in handwritten documents as the binary digits '0' and '1'. This technique maps the different sets of various input data onto the MLP output neurons. An experimental evaluation of the technique's performance is provided. This evaluation is based on the well-known 'Pen-Based Recognition of Handwritten Digits' dataset, which is comprised of a total of 250 handwriting samples that are taken from 44 writers. The results obtained are very promising for such an approach in accurate handwriting recognition.

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Handwritten digit recognition through wavelet decomposition and wavelet packet decomposition

Cited by 11 publications

References 4 publications

Wavelet based edge feature enhancement for convolutional neural networks

Wavelet based edge feature enhancement for convolutional neural networks

Handwritten Digit Recognition Using DCT and HMMs

Handwritten Digit Recognition based on Output-Independent Multi-Layer Perceptrons

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