CNN-based multilingual handwritten numeral recognition: A fusion-free approach

Gupta, Deepika; Bag, Soumen

doi:10.1016/j.eswa.2020.113784

Cited by 45 publications

(28 citation statements)

References 22 publications

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“…For the Bangla script datasets, viz, D1, D2, D3, and D4, the deep learning based techniques used to compare our results are unified CNN [40], AlexNet [41], DenseNet [43], Multi-column Multi-scale CNN (MMCNN) [45], Residual Network (ResNet-50) [47], BornoNet [48], and modified ResNet-18 [49]. Moreover, for the Devanagari script datasets, viz, D5 and D6, the deep learning based techniques used to compare our results are MMCNN [45], CNN [50], ResNet-50 [51], and Inception V3 [52].…”

Section: 69mentioning

confidence: 99%

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Sharma¹,

Kaushik²,

Gondhi³

et al. 2022

Computers, Materials &Amp; Continua

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Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources. To alleviate this shortcoming, this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization (QPSO), which is capable of automatically evolving the meaningful convolutional neural network (CNN) topologies. The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri, consisting of handwritten characters and digits. Empirically, the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

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Section: 69mentioning

confidence: 99%

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Sharma¹,

Kaushik²,

Gondhi³

et al. 2022

Computers, Materials &Amp; Continua

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“…However, the experiments were performed on individual datasets of each script, which conflicts with the goal of building a script invariant handwritten digit identification system. Similarly, [203] proposed a CNN-based approach for multilingual numeral recognition, but the experiments were limited to applying the model separately to each dataset. Future endeavors should concentrate on mixing samples from all the scripts to generate a combined dataset and then judge the performance of the model.…”

Section: Multilingual Digit Recognitionmentioning

confidence: 99%

Two Decades of Bengali Handwritten Digit Recognition: A Survey

et al. 2022

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Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to maintain the same strokes while writing any digit repeatedly, etc. In addition to that, the structural complexities of the digits of a particular language may lead to ambiguous scenarios of HDR. Over the years, researchers have developed numerous offline and online HDR pipelines, where different image processing techniques are combined with traditional Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures. Although evidence of extensive review studies on HDR exists in the literature for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few surveys on Bengali HDR (BHDR) can be found, which lack a comprehensive analysis of the challenges, the underlying recognition process, and possible future directions. In this paper, the characteristics and inherent ambiguities of Bengali handwritten digits along with a comprehensive insight of two decades of the state-of-the-art datasets and approaches towards offline BHDR have been analyzed. Furthermore, several real-life applicationspecific studies, which involve BHDR, have also been discussed in detail. This paper will also serve as a compendium for researchers interested in the science behind offline BHDR, instigating the exploration of newer avenues of relevant research that may further lead to better offline recognition of Bengali handwritten digits in different application areas.

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“…Despite rich literature on handwritten digit recognition of single-script (one language), few studies have worked on multi-script handwritten numeral recognition [8]. This problem has been addressed by previous related research by considering two main strategies.…”

Section: Introductionmentioning

confidence: 99%