Word Segmentation of Offline Handwritten Bangla Text Lines

Agarwal, Komal; Mantry, Akshat; Halder, Chayan

doi:10.1007/978-981-16-5207-3_46

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The major DAR works in Brahmi scripts are done for Bangla and Devanagari. The tasks in Bangla scripts are line, word, and character segmentation [147], [148], Online word segmentation [153], character recognition [213], [403], numeral recognition [456]. The Devanagari script DAR research include segmentation [142], [146], [169], [425], Conjunct consonants and overlapping consonants [424], Numeral recognition [193].…”

Section: Discussionmentioning

confidence: 99%

“…[133] Script-specific: Off [129]- [133], Brahmi: offline [147], [148]; Chinese: [149], Arabic [150], [151], Pri [152], On [153] Character Segmentation Stochastic Probabilistic algorithms (HMM) used to find non-linear paths between overlapping text lines: HW [134] Level set method separate the text regions from the background using partial derivatives and an external vector field in an iterative process: Pri [135], Hw, both [136]…”

Section: Watershedmentioning

confidence: 99%

See 1 more Smart Citation

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

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<p>The journey of research for Document Analysis and Recognition (DAR) started with the problem of automatic character recognition. Today, it has covered a vast span of recognition tasks such as text recognition, script identification, writer identification, word spotting, signature verification etc., in scripts like Roman, Arabic, Chinese, Japanese, Brahmi etc. Extensive advancements in deep learning techniques have achieved state-of-the-art results for various DAR tasks. This work explores the challenges from different perspectives and reviews the techniques for online/offline and printed/handwritten DAR tasks. We examine the research works with the view of script-related challenges. Various datasets for DAR are categorized into historic, printed and handwritten datasets. We present a comprehensive survey of challenges, techniques, datasets, evaluation metrics, script-related perspectives and potential future directions for DAR.</p>

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