Individuality of Bangla numerals

Halder, Chayan; Paul, Jaya

doi:10.1109/isda.2012.6416548

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Bengali, spoken by around 272 million people, is the second and the sixth most popular language in India and the world, respectively [19]. Even though it is an ancient Indo-Aryans Language [20], the representations of different Bengali numerals, as we know of and use them today, are derived from the Hindu-Arabic Numeral System [21]. Class Label 1982 1982 1953 1975 1980 1986 1981 1958 1984 1967 (e) Banglalekha-Isolated CMATERdb [29], [30] Real-life 6000 --JPEG RGB ISI Handwritten Bangla Numeral [28] Real-life 23392 --TIFF RGB NumtaDB [25] Curated 85000+ 2742 Roughly 60-40 JPEG RGB Ekush [26] Curated 30688 3086 50-50 JPEG RGB Banglalekha-Isolated [24] Curated 19748 Roughly 2000 59.4-40.6 JPEG RGB BHaND [27] Curated 70000 1750 56-44 JPEG Gray-scale make the testing process even more difficult, different challenging image artifacts such as noises, occlusions, rotations, etc.…”

Section: Characteristics Of Bengali Digitsmentioning

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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Section: Characteristics Of Bengali Digitsmentioning

confidence: 99%

Two Decades of Bengali Handwritten Digit Recognition: A Survey

et al. 2022

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“…However, previous research endeavors are conducted in closed-source datasets, resulting in a scarcity of Bengali writer recognition datasets [28]. In Bengali writer identification, efforts have been given on identifying isolated characters [29], [30]. Further, efforts have been investigated in word-level writer identification [31], as well as document-level writer recognition [28].…”

Section: Related Workmentioning

confidence: 99%

A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

Mridha¹,

Ohi

Shin

et al. 2021

IEEE Access

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Writer identification is the procedure of identifying individuals from handwriting. Writer identification is a common interest in biometric authentication and verification systems, and numerous studies are available for English, Chinese, Arabic, and Persian specific handwriting. This paper introduces a supervised offline Indic script writer identification system that can identify individuals using less than a single page of handwriting. A lightweight Convolutional Neural Network (CNN) architecture fused with non-trainable Gabor filters is used as an identification model that can recognize writers from scarce data. For the experiment, we used BanglaWriting dataset, which is openly available for Bengali writing and writer recognition. Further, we added Devanagari and Telugu datasets for evaluation. The overall evaluation shows that the proposed thresholded Gabor-based CNN architecture performs superior to numerous deep CNN architectures for Indic writer recognition.

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“…All the images are in gray tone and digitized at 300 dpi and stored in Tagged Information File Format (TIFF). [5].…”

Section: Data Collectionmentioning

confidence: 99%

“…Then using the horizontal and vertical line information we have calculated the top corner point values of each block and then we have removed the suggestive characters. After this, bounded box for each handwritten character has been calculated and these information have been stored for further processing [5]. Figure 4 shows the individual characters after automatic character extraction.…”

Section: Character Extraction Techniquementioning

confidence: 99%

Writer Identification from Handwritten Devanagari Script

Halder

Thakur

Phadikar

2015

Advances in Intelligent Systems and Computing

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This paper presents analysis of Devanagari characters for writer identification. Being originated from Brahmic script, Devanagari is the most popular script in India. It is used by over 400 million people around the world. Application of writer identification of Devanagari handwritten characters covers a vast area such as The Questioned Document Examination (QDE) is an area of the Forensic Science with the main purpose to answer questions related to questioned document (authenticity, authorship and others). Signature verification in banking, in Graphology (study of handwriting) a theory or practice for inferring a person's character, disposition, and attitudes from their handwriting. Here we collect 5 copies of handwritten characters to nullify intra-writing variation, from 50 different people mainly students. After preprocessing and character extraction, 64-dimensional feature is computed based on gradient of the images. Some manual processing is required because some noises are too difficult to remove automatically as they are much closer to the characters. We have used LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters. We have done the writer identification with all the characters and obtained 99.12 % accuracy for LIBLINEAR with all writers. Features collected from this work can be used in the next level to identify writers from their cursive writing.

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Individuality of Bangla numerals

Cited by 7 publications

References 14 publications

Two Decades of Bengali Handwritten Digit Recognition: A Survey

Two Decades of Bengali Handwritten Digit Recognition: A Survey

A Thresholded Gabor-CNN Based Writer Identification System for Indic Scripts

Writer Identification from Handwritten Devanagari Script

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