Line, Word, and Character Segmentation from Bangla Handwritten Text—A Precursor Toward Bangla HOCR

Rakshit, Payel; Halder, Chayan; Ghosh, Santosh

doi:10.1007/978-981-10-8180-4_7

Cited by 16 publications

(6 citation statements)

References 14 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%

“…Flooding the morphological surface and segmenting the input into catchment and basin: Pri, handwritten [128] Hybrid Combination of above approaches: Hist [141] Script-specific: Hist [112], [114], [115], [119], [129], [141], [142], Urdu [143], Persian [109], Chinese [124], [126], English [144], [145] Brahmi: Hw Devanagari [142], Pri Devanagari [146], Bangla [147], Gurmukhi [111] Word Segmentation Distance metric Different metrics to compute the distance between CCs (Euclidean distance, Convex hull, bounding box, average run length): Hw [129]- [131] Recognition Feedback from recognition system; Finds word boundaries based on classification algorithms (Scale space, k-means clustering, Hough transform): Pri [132], Hw [130],…”

Section: Watershedmentioning

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%

“…Segmentation-based (Explicit) [137], [138] Touched characters Recognition-based (implicit) [139], [140] Script-specific: Hist [154], CJK [155]- [157], Arabic: [150], [151], Brahmi [147], [154] that can be processed by machine learning or deep learning algorithms for solving classification problems. It preserves the information in the original input dataset and maps those features on a lower dimension.…”

Section: Touched Charactersmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Watershedmentioning

confidence: 99%

Section: Watershedmentioning

confidence: 99%

Section: Touched Charactersmentioning

confidence: 99%

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Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

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“…Rakshit P., et.al proposed a scheme for tri-level segmentation (line, word, and character) for Bengali handwritten scripts [16]. They have achieved an average of 90.46% accuracy on line segmentation, 90.06% accuracy on word segmentation, and 50.55% accuracy on character segmentation for the dataset of 50 Bengali handwritten text documents.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Optical Character Recognition for Bengali Handwritten Words

Safir¹,

Ohi²,

F.³

et al. 2021

Preprint

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Optical character recognition (OCR) is a process of converting analogue documents into digital using document images. Currently, many commercial and non-commercial OCR systems exist for both handwritten and printed copies for different languages. Despite this, very few works are available in case of recognising Bengali words. Among them, most of the works focused on OCR of printed Bengali characters. This paper introduces an end-to-end OCR system 1 for Bengali language. The proposed architecture implements an end to end strategy that recognises handwritten Bengali words from handwritten word images. We experiment with popular convolutional neural network (CNN) architectures, including DenseNet, Xception, NASNet, and MobileNet to build the OCR architecture. Further, we experiment with two different recurrent neural networks (RNN) methods, LSTM and GRU. We evaluate the proposed architecture using BanglaWritting dataset, which is a peerreviewed Bengali handwritten image dataset. The proposed method achieves 0.091 character error rate and 0.273 word error rate performed using DenseNet121 model with GRU recurrent layer.

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Implementation of Words and Characters Segmentation of Gujarati Script Using MATLAB

Gargi

Priti

2020

Innovative Data Communication Technologies and Application

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Line, Word, and Character Segmentation from Bangla Handwritten Text—A Precursor Toward Bangla HOCR

Cited by 16 publications

References 14 publications

Document Analysis and Recognition: A survey

Document Analysis and Recognition: A survey

End-to-End Optical Character Recognition for Bengali Handwritten Words

Implementation of Words and Characters Segmentation of Gujarati Script Using MATLAB

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