  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
state-of-the-art datasets and approaches towards offline BHDR have been
analyzed. Furthermore, several real-life application-specific 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.
