Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition

Tuba, Eva; Hrosik, Romana Capor; Alihodžić, Adis; Jovanović, Raka; Tuba, Milan

doi:10.1007/978-3-030-39237-6_13

Cited by 6 publications

(1 citation statement)

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“…Both handwritten and printed numerals recognition have gained considerable attention in different practical applications. Specifically, handwritten numeral recognition can be used for several purposes: (a) processing cheques in the bank sector, (b) scanning zip code in the post offices, (c) recognition of vehicle number, (d) processing medical data, and (e) recognition of the street number [ 1 , 2 , 3 , 4 ]. However, automatic recognition of handwritten numeral is to date considered to be a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Abdulhussain

Mahmmod

Naser

et al. 2021

Sensors

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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.

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