ShonkhaNet: A Dynamic Routing for Bangla Handwritten Digit Recognition Using Capsule Network

Haque, Sabrina; Rabby, Akm Shahariar Azad; Islam, Md. Sanzidul; Hossain, Syed Akhter

doi:10.1007/978-981-13-9187-3_15

Cited by 5 publications

(1 citation statement)

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“…Since the color information of the handwritten digit image has no impact on the performance of the deep learningbased model, the input is often converted to grayscale. This reduces the number of channels in the input image, reducing the training time by decreasing the computational cost [41], [56], [74]. It also ensures the unanimity of the samples collected from multiple sources [40], [56].…”

Section: ) Grayscale Conversionmentioning

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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