A Wavelet-Based Descriptor for Handwritten Numeral Classification

Seijas, Leticia M.; Segura, Enrique Carlos

doi:10.1109/icfhr.2012.174

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The data set was pre-processed by converting the images into a binary format. For feature extraction, the Biorthogonal Two-dimensional Wavelet Cohen-Daubechies-Feauveau (CDF) 9/7 was applied up to a second level of resolution, as presented in [24]. Figure 2 shows the application of the wavelet on MNIST patterns.…”

Section: A Mnist Databasementioning

confidence: 99%

Metaheuristics for feature selection in handwritten digit recognition

Seijas

Carneiro

Santana

et al. 2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

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Recognition of handwritten digits by computers is a common research topic in the pattern recognition area and has application in several domains. Many techniques can be applied in order to maximize the recognition performance, such as image preprocessing, feature extraction, feature selection and classification stages. This paper focuses on the assessment of three swarm intelligence optimization algorithms for feature selection optimization, called Binary Fish School Search (BFSS), Advanced Binary Ant Colony Optimization (ABACO) and Binary Particle Swarm Optimization (BPSO), for the recognition of handwritten digits. These meta-heuristics were applied to the well-known handwritten digit database MNIST, preprocessed with the CDF 9/7 Wavelet Transform. We used support vector machine (SVM) for the classification task. A considerable reduction in the number of features used for digit classification on the MNIST database with a small loss in the classification rates was observed.Keywords-feature selection; binary optimization; support vector machine; wavelet transform; handwritten digit recognition; binary fish school search; advanced binary ant colony optimization; binary particle swarm optimization.

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Section: A Mnist Databasementioning

confidence: 99%

Metaheuristics for feature selection in handwritten digit recognition

Seijas

Carneiro

Santana

et al. 2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

Self Cite

View full text Add to dashboard Cite

show abstract

Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network

et al. 2023

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Convolutional neural network (CNN) based methods have succeeded for handwritten numeral recognition (HNR) applications. However, CNN seems to misclassify similarly shaped numerals (i.e., the silhouette of the numerals that look the same). This paper presents an enhanced HNR system to improve the classification accuracy of the similarly shaped handwritten numerals incorporating the terminals points with CNN’s recognition, which can be utilized in various emerging applications related to language translation. In handwritten numerals, the terminal points (i.e., the start and end positions) are considered additional properties to discriminate between similarly shaped numerals. Start–End Writing Measure (SEWM) and its integration with CNN is the main contribution of this research. Traditionally, the classification outcome of a CNN-based system is considered according to the highest probability exposed for a particular numeral category. In the proposed system, along with such classification, its probability value (i.e., CNN’s confidence level) is also used as a regulating element. Parallel to CNN’s classification operation, SEWM measures the start–end points of the numeral image, suggesting the numeral category for which measured start–end points are found close to reference start–end points of the numeral class. Finally, the output label or system’s classification of the given numeral image is provided by comparing the confidence level with a predefined threshold value. SEWM-CNN is a suitable HNR method for Bengali and Devanagari numerals compared with other existing methods.

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A Wavelet-Based Descriptor for Handwritten Numeral Classification

Cited by 2 publications

References 21 publications

Metaheuristics for feature selection in handwritten digit recognition

Metaheuristics for feature selection in handwritten digit recognition

Handwritten Numeral Recognition Integrating Start–End Points Measure with Convolutional Neural Network

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