Automatic document classification and extraction system (ADoCES)

Document processing is a critical element of office automation. Document image processing begins from the Optical Character Recognition (OCR) phase with complex processing for document classification and extraction. Document classification is a process that classifies an incoming document into a particular predefined document type. Document extraction is a process that extracts information pertinent to the users from the content of a document and assigns the information as the values of the “logical structure” of the document type. Therefore, after document classification and extraction, a paper document will be represented in its digital form instead of its original image file format, which is called a frame instance. A frame instance is an operable and efficient form that can be processed and manipulated during document filing and retrieval. This dissertation describes a system to support a complete procedure, which begins with the scanning of the paper document into the system and ends with the output of an effective digital form of the original document. This is a general-purpose system with “learning” ability and, therefore, it can be adapted easily to many application domains. In this dissertation, the “logical closeness” segmentation method is proposed. A novel representation of document layout structure - Labeled Directed Weighted Graph (LDWG) and a methodology of transforming document segmentation into LDWG representation are described. To find a match between two LDWGs, string representation matching is applied first instead of doing graph comparison directly, which reduces the time necessary to make the comparison. Applying artificial intelligence, the system is able to learn from experiences and build samples of LDWGs to represent each document type. In addition, the concept of frame templates is used for the document logical structure representation. The concept of Document Type Hierarchy (DTH) is also enhanced to express the hierarchical relation over the logical structures existing among the documents

Multiple Retrieval Models and Regression Models for Prior Art Search

This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend

An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Special Libraries, February 1966

Volume 57, Issue 2https://scholarworks.sjsu.edu/sla_sl_1966/1001/thumbnail.jp

Special Libraries, December 1964

Volume 55, Issue 10https://scholarworks.sjsu.edu/sla_sl_1964/1009/thumbnail.jp

Special Libraries, February 1962

Volume 53, Issue 2https://scholarworks.sjsu.edu/sla_sl_1962/1001/thumbnail.jp

Index to Library Trends Volume 38

published or submitted for publicatio

Highly focused document retrieval in aerospace engineering : user interaction design and evaluation

Purpose – This paper seeks to describe the preliminary studies (on both users and data), the design and evaluation of the K-Search system for searching legacy documents in aerospace engineering. Real-world reports of jet engine maintenance challenge the current indexing practice, while real users’ tasks require retrieving the information in the proper context. K-Search is currently in use in Rolls-Royce plc and has evolved to include other tools for knowledge capture and management. Design/methodology/approach – Semantic Web techniques have been used to automatically extract information from the reports while maintaining the original context, allowing a more focused retrieval than with more traditional techniques. The paper combines semantic search with classical information retrieval to increase search effectiveness. An innovative user interface has been designed to take advantage of this hybrid search technique. The interface is designed to allow a flexible and personal approach to searching legacy data. Findings – The user evaluation showed that the system is effective and well received by users. It also shows that different people look at the same data in different ways and make different use of the same system depending on their individual needs, influenced by their job profile and personal attitude. Research limitations/implications – This study focuses on a specific case of an enterprise working in aerospace engineering. Although the findings are likely to be shared with other engineering domains (e.g. mechanical, electronic), the study does not expand the evaluation to different settings. Originality/value – The study shows how real context of use can provide new and unexpected challenges to researchers and how effective solutions can then be adopted and used in organizations.</p