A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Abdulhussain, Sadiq H.; Mahmmod, Basheera M.; Naser, Marwah Abdulrazzaq; Alsabah, Muntadher; Ali, Roslizah; Al-Haddad, S. A. R.

doi:10.3390/s21061999

Cited by 31 publications

(20 citation statements)

References 49 publications

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“…Orthogonal polynomials (OPs) are considered efficient tools in several applications such as information hiding [30]- [32], face recognition [21], SBD [7], speech enhancement [33], [34], and handwritten numerical recognition [35]. The moments are the projection of signals on OPs [21], [36], [37].…”

Section: Preliminariesmentioning

confidence: 99%

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.

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Section: Preliminariesmentioning

confidence: 99%

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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“…Abdulhussain et al introduce a new scheme for handwritten numeral recognition using hybrid orthogonal polynomials, where the embedded image kernel technique has been adopted in this system, and a 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 [ 8 ]. Chen et al introduce an approach for the handwritten digit recognition based on the Saak transform.…”

Section: Introductionmentioning

confidence: 99%

A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes

Qian¹,

Chi²,

Dong³

et al. 2022

Sensors

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In order to develop a non-contact and simple gesture recognition technology, a recognition method with a charge induction array of nine electrodes is proposed. Firstly, the principle of signal acquisition based on charge induction is introduced, and the whole system is given. Secondly, the recognition algorithms, including the pre-processing algorithm and back propagation neural network (BPNN) algorithm, are given to recognize three input modes of hand gestures, digital input, direction input and key input, respectively. Finally, experiments of three input modes of hand gestures are carried out, and the recognition accuracy is 97.2%, 94%, and 100% for digital input, direction input, and key input, respectively. The outstanding characteristic of this method is the real-time recognition of three hand gestures in the distance of 2 cm without the need of wearing any device, as well as being low cost and easy to implement.

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“…Similarly to image processing, speech signals processing is also essential [7], and involves several stages such as transfer [8], acquisition [9], and coding [10]. Pattern recognition, which is considered an automated process, is widely used in various applications such as computer vision [11], statistical data analysis [12], information retrieval [13], shot boundary detection [14], and bio-informatics [15]. However, the accuracy of extracting the significant features in these essential signal processing approaches is crucial [16].…”

Section: Introductionmentioning

confidence: 99%

“…Typically, discrete orthogonal moments are generated using DKPs. Discrete orthogonal moments are extensively exploited in image and signal processing [11,[32][33][34], coding theory [35], and information theory [36,37]. However, reconstructing a signal using moments and maintaining the orthogonality has to date been considered a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials

Al-Utaibi

Abdulhussain

Mahmmod

et al. 2021

Entropy

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Krawtchouk polynomials (KPs) and their moments are promising techniques for applications of information theory, coding theory, and signal processing. This is due to the special capabilities of KPs in feature extraction and classification processes. The main challenge in existing KPs recurrence algorithms is that of numerical errors, which occur during the computation of the coefficients in large polynomial sizes, particularly when the KP parameter (p) values deviate away from 0.5 to 0 and 1. To this end, this paper proposes a new recurrence relation in order to compute the coefficients of KPs in high orders. In particular, this paper discusses the development of a new algorithm and presents a new mathematical model for computing the initial value of the KP parameter. In addition, a new diagonal recurrence relation is introduced and used in the proposed algorithm. The diagonal recurrence algorithm was derived from the existing n direction and x direction recurrence algorithms. The diagonal and existing recurrence algorithms were subsequently exploited to compute the KP coefficients. First, the KP coefficients were computed for one partition after dividing the KP plane into four. To compute the KP coefficients in the other partitions, the symmetry relations were exploited. The performance evaluation of the proposed recurrence algorithm was determined through different comparisons which were carried out in state-of-the-art works in terms of reconstruction error, polynomial size, and computation cost. The obtained results indicate that the proposed algorithm is reliable and computes lesser coefficients when compared to the existing algorithms across wide ranges of parameter values of p and polynomial sizes N. The results also show that the improvement ratio of the computed coefficients ranges from 18.64% to 81.55% in comparison to the existing algorithms. Besides this, the proposed algorithm can generate polynomials of an order ∼8.5 times larger than those generated using state-of-the-art algorithms.

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A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments

Cited by 31 publications

References 49 publications

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials

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