Handwritten Bangla numeral recognition using Local Binary Pattern

Hassan, Tasnuva; Khan, Haider Adnan

doi:10.1109/iceeict.2015.7307371

Cited by 54 publications

(11 citation statements)

References 13 publications

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“…For example, a slightly tilted six ( ) might be considered as eight ( ) as they have similar baseline skeletons with different orientations. KSC algorithm [70] is used to fix individual slanted digits [47], [48].…”

Section: ) Slant Correctionmentioning

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: ) Slant Correctionmentioning

confidence: 99%

“…To remove Gaussian noise, the Gaussian filter seems to be the popular choice [36], [43], [47], [48], [56], [59], [60], [75], [76]. Finally, in order to remove small artifacts, caused due Original image taken from NumtaDB dataset [25].…”

Section: ) Noise Removalmentioning

confidence: 99%

Two Decades of Bengali Handwritten Digit Recognition: A Survey

et al. 2022

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“…A further application of OCR is provided in [44], which describes a method to recognise Bangla handwritten numerical characters using local binary pattern. Whereas in [45], the authors used OCR technique to detect printed English and Fraktur text using Long Short Term Memory (LSTM).…”

Section: Optical Character Recognitionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

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Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

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“…On their study, they have used 5000 training samples, which again validated with 1000 more samples from the same data set. In another study with the same numeral data set, [20] used LBP as their feature extraction method. Besides the shallow classifier, an adaptation of CNN or deep learning methods for Bangla handwriting numeral recognition also has been observed in recent years.…”

Section: Existing Workmentioning

confidence: 99%

Deep Hog: A Hybrid Model to Classify Bangla Isolated Alpha-Numerical Symbols

Sharif¹,

Mahboob²

2019

NNW

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Bangla is known to be the second most widely used script in the South Asian region. Despite its wide usage, a complete study with all available Bangla handwritten image classes is still due. This work proposes a hybrid model to classify all available handwritten image classes and unifying the existing benchmark datasets. The feasibility of the different handcrafted features in the hybrid model also has been demonstrated. Moreover, the proposed hybrid model obtain a maximum accuracy of 89.91 % in validation phase with a total of 259 Bangla alpha-numerical image classes. With the same number of image classes, the proposed hybrid model shows a testing accuracy of 89.28 % on 15,175 testing samples. The comparison results demonstrate that the proposed hybrid-HOG model can outperform the existing state-of-the-art classification models in Bangla handwritten alpha-numerical image classification. The code will be available on https://github.com/sharif-apu/hybrid-259.

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Handwritten Bangla numeral recognition using Local Binary Pattern

Cited by 54 publications

References 13 publications

Two Decades of Bengali Handwritten Digit Recognition: A Survey

Two Decades of Bengali Handwritten Digit Recognition: A Survey

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

Deep Hog: A Hybrid Model to Classify Bangla Isolated Alpha-Numerical Symbols

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