Handwritten text separation from annotated machine printed documents using Markov Random Fields

Peng, Xinhua; Setlur, Srirangaraj; Govindaraju, Venu; Sitaram, Ranganatha

doi:10.1007/s10032-011-0179-z

Cited by 30 publications

(19 citation statements)

References 28 publications

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“…In literature the works presented maybe classified into four different levels of text separation paragraph, text line, word and character level. Also handwritten recognition, character recognition and text localization are included as part of the study in many works [2][3][4]. Zheng et al [5] projected a method of text identification in noisy document images with relative outcomes at all levels.…”

Section: Related Workmentioning

confidence: 99%

“…In [12], G-means based classification and Markov Random Field based classification are used for patch level separation and pixel level separation of identified three categories of classes which are machine printed text, handwritten text and overlapped text. Tangila Saba [13] presented a technique based on structural and statistical features of text lines and proposed a set of classification rules to classify multilingual text lines into handwritten and printed text.…”

Section: Related Workmentioning

confidence: 99%

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Classification of Printed Text and Handwritten Characters With Neural Networks

2020

jcr

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Various scanned documents consist of machine printed text as well as handwritten alphabet. The classification of handwritten alphabet and machine printed alphabet in document images is the required process afore character recognition. We can distinguish these two types of alphabet images by their shape structural, statistical and visual difference features. This work proposes the artificial neural network based classification technique for machine printed and handwritten text classification at character level using a set of new features which are combination of statistical, shape structural and visual impression features. The projected technique attained remarkable classification efficiency on two databases; IAM dataset and prepared dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Classification of Printed Text and Handwritten Characters With Neural Networks

2020

jcr

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“…Black and white algorithms. Among them, use only binary images [9,29,[90][91][92][93][94][95][96][97][98][99].…”

Section: Feature Classificationmentioning

confidence: 99%

A comprehensive survey of mostly textual document segmentation algorithms since 2008

Eskenazi

Gomez‐Krämer

Ogier

2017

Pattern Recognition

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International audienceIn document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image segmentation algorithms. We also discuss the technical limitations of these algorithms, the way they are evaluated and the general trends of the community

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“…The method proposed in Ref. 26 contains two main steps: patch-level separation and a pixel-level separation. In patch-level separation, the entire document is modeled as a MRF, and a MRF-based classi¯cation approach is then used to separate overlapped text into machine-printed text and handwritten text by using pixel-level features.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms

Tan

Lai

Wang

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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Techniques to identify printed and handwritten text in scanned documents di®er signi¯cantly. In this paper, we address the question of how to discriminate between each type of writing on registration forms. Registration-form documents consist of various type zones, such as printed text, handwriting, table, image, noise, etc., so segmenting the various zones is a challenge. We adopt herein an approach called \ multiscale-region projection" to identify printed text and handwriting. An important aspect of our approach is the use of multiscale techniques to segment document images. A new set of projection features extracted from each zone is also proposed. The classi¯cation rules are mining and are used to discern printed text and table lines from handwritten text. The proposed system was tested on 11 118 samples in two registrationform-image databases. Some possible measures of e±ciency are computed, and in each case the proposed approach performs better than traditional methods.

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Handwritten text separation from annotated machine printed documents using Markov Random Fields

Cited by 30 publications

References 28 publications

Classification of Printed Text and Handwritten Characters With Neural Networks

Classification of Printed Text and Handwritten Characters With Neural Networks

A comprehensive survey of mostly textual document segmentation algorithms since 2008

Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms

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