Postal address block location in real time

Palumbo, Paul W.; Srihari, Sargur N.; Soh, Jung; Sridhar, R.; Demjanenko, V.

doi:10.1109/2.144438

Cited by 49 publications

(20 citation statements)

References 4 publications

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“…Document analysis operations can be speeded up by performing the operations at the connected component level, rather than at the image level. This insight was previously employed by us in locating address blocks in a real-time address block location system [6]. Thus, the first step is to generate connected components of the entire input image.…”

Section: Address Recognition Softwarementioning

confidence: 98%

Document image-processing system for name and address recognition

Srihari

Shin

Ramanaprasad

et al. 1996

Int. J. Imaging Syst. Technol.

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Section: Address Recognition Softwarementioning

confidence: 98%

Document image-processing system for name and address recognition

Srihari

Shin

Ramanaprasad

et al. 1996

Int. J. Imaging Syst. Technol.

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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.…”

mentioning

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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“…This work was partially supported by FAPESP (Phd grant 07/54201-6), CAPES/COFECUB 592/08 and ANR 07-MDCO-007-03. 1 Simon M. Lucas. http://algoval.essex.ac.uk:8080/icdar2005/ connected components are extracted over these regions, and analyzed to recover each text word.…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches have been proposed, but most of them are dedicated to specific contexts, such as automatic localization of postal addresses on envelopes [1], license plate localization [2], etc. For natural scene processing, more generic systems have been recently considered [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Snoopertext: A multiresolution system for text detection in complex visual scenes

Minetto

Thome²,

Cord³

et al. 2010

2010 IEEE International Conference on Image Processing

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Text detection in natural images remains a very challenging task. For instance, in an urban context, the detection is very difficult due to large variations in terms of shape, size, color, orientation, and the image may be blurred or have irregular illumination, etc. In this paper, we describe a robust and accurate multiresolution approach to detect and classify text regions in such scenarios. Based on generation/validation paradigm, we first segment images to detect character regions with a multiresolution algorithm able to manage large character size variations. The segmented regions are then filtered out using shapebased classification, and neighboring characters are merged to generate text hypotheses. A validation step computes a region signature based on texture analysis to reject false positives. We evaluate our algorithm in two challenging databases, achieving very good results.

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Postal address block location in real time

Cited by 49 publications

References 4 publications

Document image-processing system for name and address recognition

Document image-processing system for name and address recognition

An Overview of Bayesian Network Applications in Uncertain Domains

Snoopertext: A multiresolution system for text detection in complex visual scenes

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