Handwritten text localization in skewed documents

Kavallieratou, Ergina; Balcan, Doru C.; Popa, M.; Fakotakis, Nikos

doi:10.1109/icip.2001.959242

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…The advantage of accuracy can help visually impaired persons with head mounted device on sign boards in streets [85]. In future, BN in conjunction with features of MSER-based extracted descriptor region can also be applied to detect text in scene image video sequences [86], postal address box localization [87], automatic forms reading [88], document image analysis [89].Sensitivity analysis is an essential issue in system components which are working together to achieve a given target. To avoid abnormalities in a system, every component of a system and its operation is monitored to establish a International Journal of Computer Theory and Engineering, Vol.…”

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confidence: 99%

An Overview of Bayesian Network Applications in Uncertain Domains

Iqbal¹,

Yin²,

Hao³

et al. 2015

IJCTE

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Abstract-Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.Index Terms-Uncertainty, knowledge discovery, Bayesian network, image processing, decision making, privacy preservation, system reliability estimation. I. INTRODUCTIONUncertainty is a commonly faced problem in real world applications. Uncertainty can be described as an inadequate amount of information [1]. Nevertheless, uncertainty may also exist in situations that have enough amount of information [2]. Furthermore, uncertainty may be alleviated or eliminated with the addition of new information. Addition of more information in complex processes may lead to mining of limited knowledge. Uncertainty can be computed mathematically with probability theory. In uncertain situations, there is an involvement of possibility of states of attributes. Consequently, the models established on probabilistic inferences have the capability to assign a probabilistic value according to a defined principle. Accordingly, the prediction with large number of states in a model is accomplished. The question rises "how prediction is realized in the presence of large number of states in a model?" An answer to this question is the employment of Bayesian Network (BN) with several variables [3]- [5]. BNs, also known as belief networks, belong to the family of probabilistic graphical models. These graphical structures correspond to knowledge about an uncertain domain. More specifically, each node in the graphical structure represents a random variable, while the edges/arcs between the nodes represent conditional dependencies among nodes. These conditional dependencies are estimated by using acknowledged statistical and computational methods. Consequently, BNs incorporate concepts from graph and probability theory, computer science, and statistics.Since last two decades, BN is recognized as an important tool for a number of expert systems especially in domains involving uncertainty [6]. This recognition of BN has several reasons behind it. First, BN encodes the depen...

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confidence: 99%

An Overview of Bayesian Network Applications in Uncertain Domains

Iqbal¹,

Yin²,

Hao³

et al. 2015

IJCTE

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“…In this stage existing algorithms [8][9][10] are applied in order to perform extraction of text-lines. In this approach, we consider that there are no images, graphics or banners in the document.…”

Section: Preprocessingmentioning

confidence: 99%

“…For the discrimination and localization of text areas the algorithm described in [9] is applied. Specifically, a stage of segmentation is performed where the constrained runlength algorithm (CRLA) [11], also known as 'smearing', is used.…”

Section: Preprocessingmentioning

confidence: 99%

Discrimination of machine-printed from handwritten text using simple structural characteristics

Kavallieratou¹,

Stamatatos²

2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

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“…Polygonal regions are a recent development [24]. Some investigators employ groundtruth at the level of connected components [10], stroke fragments [8], groupings of connected components [18,22], or entire text lines [11,13]. These region definitions are perhaps suitable for many page layout analysis applications, but rectangles are too crude a granularity for segmenting handwritten words, for distinguishing noise scattered among text, or for labeling graphics as distinct from text.…”

Section: Introductionmentioning

confidence: 99%