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Journal ArticleDOI

A survey of keyword spotting techniques for printed document images

01 Feb 2011-Artificial Intelligence Review (Springer Netherlands)-Vol. 35, Iss: 2, pp 119-136
TL;DR: A survey of the past researches on character based as keyword based approaches used for retrieving information from document images to provide insights into the strengths and weaknesses of current techniques and the guidance in choosing the area that future work on document image retrieval could address.
Abstract: This paper attempts to provide a survey of the past researches on character based as keyword based approaches used for retrieving information from document images. This survey also provides insights into the strengths and weaknesses of current techniques, relevancy lies between each technique and also the guidance in choosing the area that future work on document image retrieval could address.
Citations
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Journal ArticleDOI
TL;DR: The nature of texts and inherent challenges addressed by word spotting methods are thoroughly examined and the use of retrieval enhancement techniques based on relevance feedback which improve the retrieved results are investigated.

134 citations

Journal ArticleDOI
TL;DR: The proposed survey paper specifically focuses on compressed document images and brings out two original contributions that presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain.
Abstract: The rapid growth of digital libraries, e-governance, and internet based applications has caused an exponential escalation in the volume of ‘Big-data’ particularly due to texts, images, audios and videos that are being both archived and transmitted on a daily basis. In order to make their storage and transfer efficient, different data compression techniques are used in the literature. The ultimate motive behind data compression is to transform a big size data into small size data, which eventually implies less space while archiving, and less time in transferring. However, in order to operate/analyze compressed data, it is usually necessary to decompress it, so as to bring back the data to its original form, which unfortunately warrants an additional computing cost. In this backdrop, if operating upon the compressed data itself can be made possible without going through the stage of decompression, then the advantage that could be accomplished due to compression would escalate. Further due to compression, from the data structure and storage perspectives, the original visibility structure of the data also being lost, it turns into a potential challenge to trace the original information in the compressed representation. This challenge is the motivation behind exploring the idea of direct processing on the compressed data itself in the literature. The proposed survey paper specifically focuses on compressed document images and brings out two original contributions. The first contribution is that it presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain. The second contribution is that it summarizes the different compressed domain techniques in the literature so far based on the type of compression and operations performed by them. Overall, the paper aims to provide a perspective for pursuing further research in the area of document image analysis and pattern recognition directly based on the compressed data.

46 citations


Cites background or methods from "A survey of keyword spotting techni..."

  • ...The file formats can be grouped into continuous tone still-image compression formats and bi-level still-image compression formats (Miano 1999)....

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  • ...Image compression is a technique that is used to identify internal data redundancy and to subsequently come up with a compact representation, which occupies less storage space than the original image data (Sayood 2012; Salomon et al. 2010; Miano 1999)....

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  • ...JPEG (Miano 1999) is themostwidely used image compression algorithmwhich usesDiscrete Cosine Transform (DCT)....

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  • ...The data compression techniques not only make the Big-data space efficient, but also communication efficient (download/upload); this is because transferring of a large volume of data over the internet or personal networks is expensive in terms of time and bandwidth (Kia 1997; Miano 1999)....

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  • ...Digitally acquired document images generally occupy a large amount of storage space and thus require an excess of download/transfer time (Kia 1997; Gonzalez and Woods 2009; Miano 1999)....

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Proceedings ArticleDOI
13 May 2013
TL;DR: This paper proposes Hybrid algorithm which combine the advantage of ACO and Cuckoo search and shows that Hybrid algorithm performs well than compared with the ACO algorithm in terms of performance of the algorithm and makespan.
Abstract: Cloud computing technologies offer major benefits to the IT industries in terms of elasticity and rapid provisioning, pay-as-you-go-model, reduced capital cost, access to unlimited resources, flexibility. Job scheduling is a combinatorial optimization problem in the fields of computer science where the ideal jobs are assigned to required resource at a particular instant of time. In this paper we proposed Hybrid algorithm which combine the advantage of ACO and Cuckoo search. The makespan or completion time can be reduced with the help of hybrid algorithm, since the jobs have been executed with in the specified time interval by allocation of required resources using the Hybrid algorithm. The obtain results shows that Hybrid algorithm performs well than compared with the ACO algorithm in terms of performance of the algorithm and makespan.

35 citations


Cites background from "A survey of keyword spotting techni..."

  • ...Minimization of makespan can be done by assigning the set of Ji jobs to set of virtual machines ’ vm’, the order of execution of the jobs in virtual machines does not matters....

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Book
12 Jul 2013
TL;DR: This monograph surveys and appraises techniques for pattern matching in compressed text and images, and identifies the important relationship between pattern matching and compression, and proposes some performance measures for compressed pattern matching algorithms.
Abstract: Pattern Matching in Compressed Texts and Images surveys and appraises techniques for pattern matching in compressed text and images. Normally compressed data needs to be decompressed before it is processed. If however the compression has been done in the right way, it is often possible to search the data without having to decompress it, or, at least, only partially decompress it. The problem can be divided into lossless and lossy compression methods, and then in each of these cases the pattern matching can be either exact or inexact. Much work has been reported in the literature on techniques for all of these cases. It includes algorithms that are suitable for pattern matching for various compression methods, and compression methods designed specifically for pattern matching. This monograph provides a survey of this work while also identifying the important relationship between pattern matching and compression, and proposing some performance measures for compressed pattern matching algorithms. Pattern Matching in Compressed Texts and Images is an excellent reference text for anyone who has an interest in the problem of searching compressed text and images. It concludes with a particularly insightful section on the ideas and research directions that are likely to occupy researchers in this field in the short and long term.

30 citations


Cites methods from "A survey of keyword spotting techni..."

  • ...Methods have thus been proposed for document analysis and retrieval directly on the document images, without the need for OCR— see for example [103, 228, 255]....

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Journal ArticleDOI
TL;DR: The experimental results show that the proposed two-step segmentation-free word spotting method for historical printed documents outperforms significantly the competitive approaches.
Abstract: In this paper, a two-step segmentation-free word spotting method for historical printed documents is presented. The first step involves a minimum distance matching between a query keyword image and a document page image using keypoint correspondences. In the second step of the method, the matched keypoints on the document image serve as indicators for creating candidate image areas. The query keyword image is matched against the candidate image areas in order to properly estimate the bounding boxes of the detected word instances. The method is evaluated using two datasets of different languages and is compared against segmentation-free state-of-the-art methods. The experimental results show that the proposed method outperforms significantly the competitive approaches.

24 citations


Cites background from "A survey of keyword spotting techni..."

  • ...Profile features, such as upper or lower word profiles, projection, density or transition profiles have been reported to successfully represent words in a document image that has undergone word level segmentation [24, 26]....

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References
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Journal ArticleDOI
TL;DR: A survey of methods developed by researchers to access and manipulate document images without the need for complete and accurate conversion is provided.

319 citations


"A survey of keyword spotting techni..." refers methods in this paper

  • ...Optical Character Recognition deals with the machine recognition of characters present in an input image obtained using scanning operation (Doermann 1998)....

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  • ...This kind of technique is also called as Keyword Spotting, in which the text is identified at word level using the properties of image Doermann (1998)....

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  • ...1999) and Document Image Retrieval (Keyword spotting) technique (Doermann 1998)....

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  • ...There are two primary approaches to locate the desirable text in the document images for retrieving the appropriate information; Optical Character Recognition (OCR) technique (Kameshiro et al. 1999) and Document Image Retrieval (Keyword spotting) technique (Doermann 1998)....

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Journal ArticleDOI
A.L. Spitz1
TL;DR: This work has developed techniques for distinguishing which language is represented in an image of text using a technique based on character shape codes, a representation of Latin text that is inexpensive to compute.
Abstract: Most document recognition work to date has been performed on English text. Because of the large overlap of the character sets found in English and major Western European languages such as French and German, some extensions of the basic English capability to those languages have taken place. However, automatic language identification prior to optical character recognition is not commonly available and adds utility to such systems. Languages and their scripts have attributes that make it possible to determine the language of a document automatically. Detection of the values of these attributes requires the recognition of particular features of the document image and, in the case of languages using Latin-based symbols, the character syntax of the underlying language. We have developed techniques for distinguishing which language is represented in an image of text. This work is restricted to a small but important subset of the world's languages. The method first classifies the script into two broad classes: Han-based and Latin-based. This classification is based on the spatial relationships of features related to the upward concavities in character structures. Language identification within the Han script class (Chinese, Japanese, Korean) is performed by analysis of the distribution of optical density in the text images. We handle 23 Latin-based languages using a technique based on character shape codes, a representation of Latin text that is inexpensive to compute.

279 citations


"A survey of keyword spotting techni..." refers background in this paper

  • ...Still, three shape codes have been further explored into five shape codes named as V0, to distinguish the character j from i (Smeaton and Spitz 1997; Spitz 1993)....

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Proceedings ArticleDOI
03 Aug 2003
TL;DR: A range of features suitable for matching words using dynamic time warping (DTW) are analyzed, which aligns and compares sets of features extracted from two images and outperforms competing techniques in speed and precision.
Abstract: For the transition from traditional to digital libraries, the large number of handwritten manuscripts that exist pose a great challenge. Easy access to such collections requires an index, which is currently created manually at great cost. Because automatic handwriting recognizers fail on historical manuscripts, the word spotting technique has been developed: the words in a collection are matched as images and grouped into clusters which contain all instances of the same word. By annotating "interesting" clusters, an index that links words to the locations where they occur can be built automatically. Due to the noise in historical documents, selecting the right features for matching words is crucial. We analyzed a range of features suitable for matching words using dynamic time warping (DTW), which aligns and compares sets of features extracted from two images. Each feature's individual performance was measured on a test set. With an average precision of 72%, a combination of features outperforms competing techniques in speed and precision.

234 citations


"A survey of keyword spotting techni..." refers methods in this paper

  • ...Dynamic Time Warping is a dynamic programming based procedure (Rath and Manmatha 2003) to align two sequences of signals and compute a similarity measure....

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Journal ArticleDOI
TL;DR: This paper briefly describes various components of a document analysis system and provides the background necessary to understand the detailed descriptions of specific techniques presented in other papers in this issue.
Abstract: Document image analysis refers to algorithms and techniques that are applied to images of documents to obtain a computer-readable description from pixel data. A well-known document image analysis product is the Optical Character Recognition (OCR) software that recognizes characters in a scanned document. OCR makes it possible for the user to edit or search the document’s contents. In this paper we briefly describe various components of a document analysis system. Many of these basic building blocks are found in most document analysis systems, irrespective of the particular domain or language to which they are applied. We hope that this paper will help the reader by providing the background necessary to understand the detailed descriptions of specific techniques presented in other papers in this issue.

143 citations