A character segmentation algorithm for the palm leaf manuscripts

Ge, Peng; Yu, Pengfei; Li, Haiyan; Li, HongSong; Zhu, Xudong

doi:10.1109/ciapp.2017.8167238

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…The third step of OCR is image pre-processing it is used in several processes such as remove the noise of the images, blur removal (blur images can be changed into clear images), binate, scattering, boundary detection and some morphological processes [3], so Optical Character Recognition is getting a prepared image of the script region which is free from noise and blur.…”

Section: Image Pre-processingmentioning

confidence: 99%

“…After analyzing the segmentation, generally, it applies word-based segmentation to perform verbatim wise separation of any handwritten document images at lost all the word-based segmentation it is processing by character segmentation to analyze the character in any scanned, printed Indian vernacular languages manuals and images. In order to address the resolutions, it formulates the word segmentation, resolution as in an encoding quadric assignment resolution that are Considered as pair wise ratifications between the Edges as well as hoods of individual gaps [3].…”

Section: Introductionmentioning

confidence: 99%

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International, National and Local Languages OCR Segmentation of Running Hand Scripts

Kumar¹,

Chitra²

2020

IJEAT

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Script Segmentation of running hand scripts is a complicated job because of different hand writing styles and complex formational quality. Segmentation of moving hand script in Indian language is a difficult assignment .The incident of a title, crossway nature in the mid section & half nature makes the segmentation process is more difficult. Sometimes, the import space and noises make line shatter a hard task. Without disconnecting the nudge characters, it will be complicated to recognize the character; hence shatter is needed for the moving texts in a word. So, the technique, according to that first step will be pre-processing of a term, then we can identify the joint points, form the bounding boxes around all perpendicular & parallel lines of the script , finally splint the nudge nature based on their height and width. For non-touching printed scripts and running hand scripts, it gives us 95% and 90%result respectively. This technique fails for some hand written characters because of obligation that means cause will not match always to a lot of variation in the different handwriting scripts i.e., character like occurs problem as space in the middle of parts in scripts that can be resolve by identification system because of this system considers initial part as non-bar character and cut after that, as the future exertion is a concern, in this go these different characters as input to the character identification system. In this method have resolute control of a word which will once more helpful for the identification system to perceive bottom modifiers and higher modifiers. This running hand scripts technique can be applicable to other Indic languages hand written scripts.

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Section: Image Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%