A Deep Convolutional Neural Network for Bangla Handwritten Numeral Recognition

Islam, Kazi Mejbaul; Noor, Rouhan; Saha, Chaity; Rahimi, Jakaria

doi:10.1109/wiecon-ece.2018.8783151

Cited by 4 publications

(7 citation statements)

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“…Another way is to use a sharpening kernel and perform 2D convolution on the image. For example, [76] and [77] used Laplacian filter to extract edge information, which is then added to the original image to sharpen blurred samples.…”

Section: ) Deblurringmentioning

confidence: 99%

“…9(b)). Hence, the existing literature on BHDR rotated the digit images by a randomly chosen rotation angle within a range of 0 to 50 degrees on either direction [36], [39], [42], [61]- [63], [73], [77], [82]. One key consideration is that, after performing rotation, additional pixels are padded to preserve the original dimension of the image [73].…”

Section: A Geometric Transformation 1) Rotation and Flippingmentioning

confidence: 99%

“…9(d)). In BHDR, 0 to 30% zooming augmentation can be seen [56], [62], [63], [73], [77], [78]. Shifting requires translating each pixel of a sample by a constant factor chosen randomly within a certain range (Fig.…”

Section: A Geometric Transformation 1) Rotation and Flippingmentioning

confidence: 99%

“…[61]- [63], [73], [77], [78], [82]. In this process, the pixels that are shifted outside the image boundary are discarded and the pixels that become empty are filled with constant values or values of the nearest pixel.…”

Section: A Geometric Transformation 1) Rotation and Flippingmentioning

confidence: 99%

“…The magnitude can also be controlled using a constant factor. Common values for constant factors are often in the range of 0 to 0.25 [61], [62], [77]. Shearing in this manner can help the model learn to recognize slanted texts.…”

Section: ) Distortionmentioning

confidence: 99%

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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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Section: ) Deblurringmentioning

confidence: 99%