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Integrating visual and textual cues for query-by-string word spotting
- In: Proceedings of the International Conference on Document Analysis and Recognition
, 2013
"... Abstract—In this paper, we present a word spotting frame-work that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character n-grams while the visual one is based on the bag-of-vis ..."
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Abstract—In this paper, we present a word spotting frame-work that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character n-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outper-forming state-of-the-art performances. I.
Exploring Digital Libraries with Document Image Retrieval
"... Abstract. In this paper, we describe a system to perform Document Image Retrieval in Digital Libraries. The system allows users to retrieve digitized pages on the basis of layout similarities and to make textual searches on the documents without relying on OCR. The system is discussed in the context ..."
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Abstract. In this paper, we describe a system to perform Document Image Retrieval in Digital Libraries. The system allows users to retrieve digitized pages on the basis of layout similarities and to make textual searches on the documents without relying on OCR. The system is discussed in the context of recent applications of document image retrieval in the field of Digital Libraries. We present the different techniques in a single framework in which the emphasis is put on the representation level at which the similarity between the query and the indexed documents is computed. We also report the results of some recent experiments on the use of layout-based document image retrieval. 1
Self-Organizing Maps for Clustering in Document Image Analysis
"... Summary. In this chapter, we discuss the use of Self Organizing Maps (SOM) to deal with various tasks in Document Image Analysis. The SOM is a particular type of artificial neural network that computes, during the learning, an unsupervised clustering of the input data arranging the cluster centers i ..."
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Summary. In this chapter, we discuss the use of Self Organizing Maps (SOM) to deal with various tasks in Document Image Analysis. The SOM is a particular type of artificial neural network that computes, during the learning, an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning in document image analysis, we present our recent work in the field. We describe the use of the SOM at three processing levels: the character clustering, the word clustering, and the layout clustering, with applications to word retrieval, document retrieval and page classification. In order to improve the clustering effectiveness, when dealing with small training sets, we propose an extension of the SOM training algorithm that considers the tangent distance so as to increase the SOM robustness with respect to small transformations of the patterns. Experiments on the use of this extended training algorithm are reported for both character and page layout clustering. 1
Enabling Search over Large Collections of Telugu Document Images – An Automatic Annotation Based Approach
"... Abstract. For the first time, search is enabled over a massive collection of 21 Million word images from digitized document images. This work advances the state-of-the-art on multiple fronts: i) Indian language document images are made searchable by textual queries, ii) interactive content-level acc ..."
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Abstract. For the first time, search is enabled over a massive collection of 21 Million word images from digitized document images. This work advances the state-of-the-art on multiple fronts: i) Indian language document images are made searchable by textual queries, ii) interactive content-level access is provided to document images for search and retrieval, iii) a novel recognition-free approach, that does not require an OCR, is adapted and validated iv) a suite of image processing and pattern classification algorithms are proposed to efficiently automate the process and v) the scalability of the solution is demonstrated over a large collection of 500 digitised books consisting of 75,000 pages. Character recognition based approaches yield poor results for developing search engines for Indian language document images, due to the complexity of the script and the poor quality of the documents. Recognition free approaches, based on word-spotting, are not directly scalable to large collections, due to the computational complexity of matching images in the feature space. For example, if it requires 1 mSec to match two images, the retrieval of documents to a single query, from a large collection like ours, would require close to a day’s time. In this paper we propose a novel automatic annotation based approach to provide textual description of document images. With a one time, offline computational effort, we are able to build a text-based retrieval system, over annotated images. This system has an interactive response time of about 0.01 second. However, we pay the price in the form of massive offline computation, which is performed on a cluster of 35 computers, for about a month. Our procedure is highly automatic, requiring minimal human intervention. 1
Document Image Indexing using Edit Distance based Hashing
, 2011
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
PCA-Based PCA Based Relevance Feedback in Document Image Retrieval
"... Research has been devoted in the past few years to relevance feedback as an effective solution to improve performance of information retrieval systems. Relevance feedback refers to an interactive process that helps to improve the retrieval performance. In this paper we propose the use of relevance f ..."
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Research has been devoted in the past few years to relevance feedback as an effective solution to improve performance of information retrieval systems. Relevance feedback refers to an interactive process that helps to improve the retrieval performance. In this paper we propose the use of relevance feedback to improve document image retrieval System (DIRS) performance. This paper compares a variety of strategies for positive and negative feedback. In addition, feature subspace is extracted and updated during the feedback process using a Principal Component Analysis (PCA) technique and based on user’s feedback. That is, in addition to reducing the dimensionality of feature spaces, a proper subspace for each type of features is obtained in the feedback process to further improve the retrieval accuracy. Experiments show that using relevance Feedback in DIR achieves better performance than common DIR.
IJDAR DOI 10.1007/s10032-008-0067-3 ORIGINAL PAPER
"... Matching word images for content-based retrieval from printed document images ..."
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Matching word images for content-based retrieval from printed document images
A Survey of Document Image Retrieval in Digital Libraries
"... In this paper, we analyze the current trends in the applications of Document Image Retrieval techniques in the field of Digital Libraries. We present the different techniques in a single framework in which the emphasis is put on the representation level at which the similarity between the query and ..."
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In this paper, we analyze the current trends in the applications of Document Image Retrieval techniques in the field of Digital Libraries. We present the different techniques in a single framework in which the emphasis is put on the representation level at which the similarity between the query and the indexed documents is computed.
Transformation invariant SOM clustering in Document Image Analysis
"... In this paper, we propose the combination of the Self Organizing Map (SOM) and of the tangent distance for effective clustering in Document Image Analysis. The proposed model (SOM TD) is used for character and layout clustering, with applications to word retrieval and to page classification. By usin ..."
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In this paper, we propose the combination of the Self Organizing Map (SOM) and of the tangent distance for effective clustering in Document Image Analysis. The proposed model (SOM TD) is used for character and layout clustering, with applications to word retrieval and to page classification. By using the tangent distance it is possible to improve the SOM clustering so as to be more tolerant with respect to small local transformations of the input patterns. 1.
Embedded Map Projection for Dimensionality Reduction-Based Similarity Search
"... Abstract. We describe a dimensionality reduction method based on data point projection in an output space obtained by embedding the Growing Hierarchical Self Organizing Maps (GHSOM) computed from a training data-set. The dimensionality reduction is used in a similarity search framework whose aim is ..."
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Abstract. We describe a dimensionality reduction method based on data point projection in an output space obtained by embedding the Growing Hierarchical Self Organizing Maps (GHSOM) computed from a training data-set. The dimensionality reduction is used in a similarity search framework whose aim is to efficiently retrieve similar objects on the basis of the Euclidean distance among high dimensional feature vectors projected in the reduced space. This research is motivated by applications aimed at performing Document Image Retrieval in Digital Libraries. In this paper we compare the proposed method with other dimensionality reduction techniques evaluating the retrieval performance on three data-sets. 1
