Detection of Bold and Italic Character in Devanagari Script

Yadav, Ravi Kant; Mazumdar, Bireshwar Dass

doi:10.5120/4792-7037

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…It first extracted the normalized total height of the vertical straight line segments (VSLS) and the normalized total length of the long continuous diagonal strokes (CDS). R. K. Yadav et al [5] described a simple and fast algorithm for detection of italic and bold characters in Devnagari script, without recognition of the actual character. N. Sharma et al [6] also presented a technique for improving the recognition accuracy of Hindi OCR system by developing the concept for detection of bold, italic and underlined words.…”

Section: Introductionmentioning

confidence: 99%

A two-stage style detection approach for printed Roman script words

Singh

Jana

Sarkar

et al. 2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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Development of Optical Character Recognition (OCR) for printed Roman script is still an active area of research.Automatic Style Identification (ASI) can be used to improve the performance of OCR system and keyword spotting techniques for printed Roman script. This paper proposes a two stage font invariant technique for detection of italic, bold, underlined, normal and all capital styled words for printed Roman script. In the first stage, the technique separates the underlined words from non-underlined words. In the second stage, a 25-element feature set has been extracted from the non-underlined words to identify the other said styled words which are evaluated using multiple classifiers. The technique has been tested on 2100 words printed in five well-known fonts namely, Arial, Cambria, Calibri, Gill Sans, and Times New Roman in which each of the font contributes exactly about 420 words. Based on the identification accuracies of multiple classifiers, Multi Layer Perceptron (MLP) classifier has been chosen as the final classifier which was tested comprehensively using different folds and with different number of epochs. Overall accuracy of the system is found to be 98.25% using 3-fold cross validation scheme.

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Section: Introductionmentioning

confidence: 99%

A two-stage style detection approach for printed Roman script words

Singh

Jana

Sarkar

et al. 2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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“…Among these schemes, two essentially unusual methods have can be examined. Here in this paper present [1] a new propose a method that follows the first approach, i.e. it segments complete lines of printed text into single words.…”

Section: Introductionmentioning

confidence: 99%

“…Segmenting a on paper text into lines and essentially to extra segment it into particular declarations is easier said than done and error prone for the reason that it is a pure bottom-up procedure and no high stage information about the text is reachable. The word segmentation algorithm used in our method includes some designs from [1].Word recognition form scanned or printed image has been also became a significant job in image processing. That's why segmentation based recognition algorithm is explained in [2] [3].Both produce the idea for off-line handwritten text.…”

Section: Introductionmentioning

confidence: 99%

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2016

IJDPS

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Cohesive Multi-Oriented Text Detection and Recognition Structure in Natural Scene Images Regions has Exposed

Siddiqui¹,

Namdeo²

2016

IJDPS

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Scene text recognition brings various new challenges occurs in recent years. Detecting and recognizing text in scenes entails some of the equivalent problems as document processing, but there are also numerous novel problems to face for recognizing text in natural scene images. Recent research in these regions has exposed several promise but present is motionless much effort to be entire in these regions. Most existing techniques have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a new scheme which detects texts of arbitrary directions in natural scene images. Our algorithm is equipped with two sets of characteristics specially designed for capturing both the natural characteristics of texts using MSER regions using Otsu method. To better estimate our algorithm and compare it with other existing algorithms, we are using existing MSRA Dataset, ICDAR Dataset, and our new dataset, which includes various texts in various real-world situations. Experiments results on these standard datasets and the proposed dataset shows that our algorithm compares positively with the modern algorithms when using horizontal texts and accomplishes significantly improved performance on texts of random orientations in composite natural scenes images.

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Detection of Bold and Italic Character in Devanagari Script

Cited by 3 publications

References 9 publications

A two-stage style detection approach for printed Roman script words

A two-stage style detection approach for printed Roman script words

Untitled

Cohesive Multi-Oriented Text Detection and Recognition Structure in Natural Scene Images Regions has Exposed

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