Text Image Spotting Using Local Crowdedness and Hausdorff Distance.
ABSTRACT This paper investigates a Hausdorff distance, which is used for measurement of image similarity, to see whether it is also
effective for document image retrieval. We proposed a method using a local crowdedness algorithm and a modified Hausdorff
distance which has an ability of detection of partial text image in a document image. We found that the proposed method achieved
a reliable performance of text spotting on postal envelops.
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ABSTRACT: In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very di#erent from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.
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ABSTRACT: With the rising popularity and importance of document images as an information source, information retrieval in document image databases has become a growing and challenging problem. In this paper, we propose an approach with the capability of matching partial word images to address two issues in document image retrieval: word spotting and similarity measurement between documents. First, each word image is represented by a primitive string. Then, an inexact string matching technique is utilized to measure the similarity between the two primitive strings generated from two word images. Based on the similarity, we can estimate how a word image is relevant to the other and, thereby, decide whether one is a portion of the other. To deal with various character fonts, we use a primitive string which is tolerant to serif and font differences to represent a word image. Using this technique of inexact string matching, our method is able to successfully handle the problem of heavily touching characters. Experimental results on a variety of document image databases confirm the feasibility, validity, and efficiency of our proposed approach in document image retrieval.IEEE Transactions on Knowledge and Data Engineering 12/2004; 16(11):1398- 1410. DOI:10.1109/TKDE.2004.76 · 1.82 Impact Factor
Conference Paper: Forensic Handwritten Document Retrieval System.[Show abstract] [Hide abstract]
ABSTRACT: Document storage and retrieval capabilities of the CEDAR-FOX forensic handwritten document examination system are described. The system is designed for automated and semiautomated analysis of scanned handwritten documents. For library creation, the system provides functionalities for (i) entering document metadata, e.g., identification number, writer and other collateral information, (ii) creating a textual transcript of the image content at the word level, and (iii) including automatically extracted document level features, e.g., stroke width, slant, word gaps, as well as finer features that capture the structural characteristics of characters and words. For extracting these features the system performs page analysis, page segmentation, line separation, word segmentation and finally recognition of characters and words. The extracted features are used for writer identification by matching against a library built as a database. The system design is driven by questioned document examination with its emphasis on writer identification. Several query modalities are permitted for retrieval: (i) document level: the entire document image is the query; (ii) partial image: a region of interest (ROI) of a document; (ii) word image: which is also called word spotting; (iv) text keyword: the user can type in keywords ranging from the words in the documents, case number, person names, time and the preregistered keywords such as brief descriptions of the case. The system has been implemented using Microsoft visual C++ and tested using MySQL database system from MySQL ABTM. It provides as a graphical user interface for forensic document identification, verification and analysis.1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), 23-24 January 2004, Palo Alto, CA, USA; 01/2004