Text line segmentation of unconstrained handwritten Kannada script

Aradhya, V. N. Manjunath; Naveena, C.

doi:10.1145/1947940.1947989

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The proposed method first extracts individual handwritten text lines present in the document image using the method described in [1]. The method has been implemented in MATLAB 7.0 on a Pentium IV 3.0 GHz with 1GB RAM.…”

Section: Experimental Results and Comparative Studymentioning

confidence: 99%

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Skew Estimation for Unconstrained Handwritten Documents

Aradhya

Naveena

Niranjan

2011

Advances in Computing and Communications

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Document skew estimation is one of the most important and challenging phase in OCR system. Skew estimation of handwritten documents is still remains challenging in the field of document image analysis due to a non-uniform text line. Hence, in this paper, we present a novel scheme for handwritten documents. The proposed method is based on mixture models. The expectation-maximization (EM) algorithm is used to learn the mixture of Gaussians. Subsequently the cluster means obtained from the individual words is used estimate the skew angle. Experiments on different handwritten documents and documents corrupted by noise shows the effectiveness of the proposed method.

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Section: Experimental Results and Comparative Studymentioning

confidence: 99%

“…We applied algorithm, which is described in [1] for extracting text line from the documents. We applied algorithm, which is described in [1] for extracting text line from the documents.…”

Section: Experimental Results and Comparative Studymentioning

confidence: 99%

Skew Estimation for Unconstrained Handwritten Documents

Aradhya

Naveena

Niranjan

2011

Advances in Computing and Communications

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“…These techniques of text line segmentation are also applied to the Indian languages. Aradhya et al [18] discussed the challenging issue in handwritten documents which are caused due to skew and curves in the text lines. Authors focused on these issues and presented a novel approach for cursive handwritten Kannada script.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

2019

ijrte

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Segmentation is always an important step in designing an Optical Character Recognition (OCR) of any script. In this paper, we focus on the line and word segmentation in typewritten Gurmukhi script documents. In order to perform this task, we consider OCR based methodology where several processing steps are implemented. The typewritten documents suffer from several issues such as noise, skew, and quality of the document. In this work, we present a combined pre-processing scheme where document thresholding and skew detection and correction schemes are implemented where image thresholding is obtained using Niblack’s method and skew correction is carried out using gradient histogram algorithm and uniform orientation is obtained. Later, line segmentation scheme is applied where probability density function is applied to generate the text distribution in the probability map. Here, identifying the relation of the text to the exact line is a challenging task hence, we present a 2D-Gaussian modelling which helps to identify the text boundaries in the x and y direction. The proposed methodology is applied for typewritten Gurmukhi documents and an experimental study is carried out to show that the proposed approach achieves better performance when compared with the existing techniques

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