A novel prototype generation technique for handwriting digit recognition

Impedovo, S.; Mangini, F; Barbuzzi, Donato

doi:10.1016/j.patcog.2013.04.016

Cited by 29 publications

(4 citation statements)

References 29 publications

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“…A recent study includes novel prototype generation technique to recognize handwritten digits in two stages. An adaptive resonance theory-based algorithm used in first stage for an initial solution and second stage solve optimization problem associated to classification [7]. A hybrid convolution neural network and support vector machine model that automatically retrieves features based on convolution neural network is a recent study to recognize scanned digits [25].…”

Section: Introductionmentioning

confidence: 99%

A combined static and dynamic feature extraction technique to recognize handwritten digits

Sharma

2015

Vietnam J Comput Sci

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The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (<1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

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Section: Introductionmentioning

confidence: 99%

A combined static and dynamic feature extraction technique to recognize handwritten digits

Sharma

2015

Vietnam J Comput Sci

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“…Impedovo et al [18] introduce a handwriting digit recognition PG algorithm. This algorithm consists of two phases.…”

Section: Related Workmentioning

confidence: 99%

Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms

et al. 2022

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The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing the initial training data into subsets until all of them become homogeneous, i.e., they contain instances of the same class. To divide a non-homogeneous subset, the algorithm computes its two furthest instances and assigns all instances to their closest furthest instance. This is a very expensive computational task, since all distances among the instances of a non-homogeneous subset must be calculated. Moreover, noise in the training data leads to a large number of small homogeneous subsets, many of which have only one instance. These instances are probably noise, but the algorithm mistakenly generates prototypes for these subsets. This paper proposes simple and fast variations of RSP3 that avoid the computationally costly partitioning tasks and remove the noisy training instances. The experimental study conducted on sixteen datasets and the corresponding statistical tests show that the proposed variations of the algorithm are much faster and achieve higher reduction rates than the conventional RSP3 without negatively affecting the accuracy.

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“…Instance abstraction, also known as prototype generation, generates and replaces the original data with new artificial data [46]. Most of the instance abstraction methods use merging or divide-and-conquer strategies to set new artificial samples [47], or are based on clustering approaches [48], Learning Vector Quantization (LVQ) hybrids [49], advanced proposals [50], [51], [52], and evolutionary algorithms-based schemes [53], [54], [55].…”

Section: Instance Reductionmentioning

confidence: 99%

Local Distribution in Neighborhood for Classification

Mao,

Hu,

Chen

et al. 2018

Preprint

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The k -nearest-neighbor method performs classification tasks for a query sample based on the information contained in its neighborhood. Previous studies into the k -nearest-neighbor algorithm usually achieved the decision value for a class by combining the support of each sample in the neighborhood. They have generally considered the nearest neighbors separately, and potentially integral neighborhood information important for classification was lost, e.g. the distribution information. This article proposes a novel local learning method that organizes the information in the neighborhood through local distribution. In the proposed method, additional distribution information in the neighborhood is estimated and then organized; the classification decision is made based on maximum posterior probability which is estimated from the local distribution in the neighborhood. Additionally, based on the local distribution, we generate a generalized local classification form that can be effectively applied to various datasets through tuning the parameters. We use both synthetic and real datasets to evaluate the classification performance of the proposed method; the experimental results demonstrate the dimensional scalability, efficiency, effectiveness and robustness of the proposed method compared to some other state-of-the-art classifiers. The results indicate that the proposed method is effective and promising in a broad range of domains.

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A novel prototype generation technique for handwriting digit recognition

Cited by 29 publications

References 29 publications

A combined static and dynamic feature extraction technique to recognize handwritten digits

A combined static and dynamic feature extraction technique to recognize handwritten digits

Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms

Local Distribution in Neighborhood for Classification

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