Extraction of Sequence from Bangla Handwritten Numerals and Recognition Using LSTM

Hye, Shadmaan; Rahat-uz-Zaman, Md.; Akhand, M. A. H.

doi:10.1109/tensymp50017.2020.9230921

Cited by 2 publications

(2 citation statements)

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“…Previous research works have proven CNN-based architectures to be very effective for the image classification task. However, various authors reported that CNN-based architectures tend to misclassify specific Bengali digits where the shape and size are similar (such as ' ' and ' ') [69], [162], [180]. To improve the overall performance, the efficiency of RNN-based architectures has also been explored for its ability to learn contextual information through recurrent connections.…”

Section: B Recurrent Neural Network-based Architecturesmentioning

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: B Recurrent Neural Network-based Architecturesmentioning

confidence: 99%

“…In another work, the authors took a unique preprocessing approach that converted the input image into a directed acyclic graph with a fixed number of equidistant points [180]. Then it was fed to an LSTM architecture containing two LSTM layers with 50 and 30 units respectively.…”

Section: Unfoldmentioning

confidence: 99%