11808205
Mending broken handwriting with a macrostructure analysis method to improve recognition
Abstract Broken characters always create problems in handwriting recognition systems, especially those using boundary and/or skeleton information. This paper presents a macrostructure analysis (MSA) mending method based on skeleton and boundary information and an MSA that investigates the stroke tending direction and other properties of handwritings. A new skeleton end extension algorithm is introduced, which compensates the defectiveness of the skeletonization algorithm and obtains a satisfactory skeleton. When combined with suitable parameters, improved performance from a handwriting classifier is achieved. The experimental results from over 13 000 numerals show the efficiency and robustness of the proposed method, raising recognition rates by over 10% for broken handwritten digits, from 74.8% to 86.4%.
21940463
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural "gas" classifiers
Abstract This paper presents a hybrid method for handwritten numeral recognition that combines two compensatory recognition algorithms by analysing their performance for several aspects. The skeleton-based structural recognition algorithm employed in this method is robust under distortion but sensitive to noise and flaws. On the other hand, the neural network classifier, which uses scaled binary images as features and the neural “gas” model for classification, is relatively immune to noise and flaws but sensitive to distortion. The different performances of the two algorithms for broken, connected or slanted numerals, and the measurement-level decision provided by the neural network are detected and combined with different strategies to develop matching rules for each recognition method. Five combination methods based on performance analysis are developed to meet different requirements. As the two algorithms have fairly compensatory properties, the proposed method improves the recognition rate and reliability by exploiting the advantages and avoiding the weaknesses of each classifier. The experimental results from a large set of data show the efficiency and robustness of the proposed method.
44970162
Toward Web mining of cross-language query translations in digital libraries
This paper proposes an effective query-translation approach that enables a cross-language information retrieval (CLIR) service to be more easily supported in digital library systems that only contain monolingual content. A query-translation engine called LiveTrans is used to process the translation requests of cross-lingual queries from connected digital library systems. To automatically extract translations not covered by standard dictionaries, the engine is developed based on a novel integration of dictionary resources and Web mining approaches, including anchor-text and search-result methods. The engine exploits a broad range of multilingual Web resources used as live bilingual corpora to alleviate translation difficulties. It is shown to be particularly effective for extracting multilingual translation equivalents of query terms containing proper names or new terminology. The obtained results show the feasibility of and great potential for creating English-Chinese CLIR services in existing digital libraries and new applications in cross-language Web searching, although difficulties still remain that need to be investigated further.
39453122
Xml Clustering And Retrieval Through Principal Component Analysis
XML is increasingly important in data exchange and information management. A great deal of efforts have been spent in developing efficient techniques for storing, querying, indexing and accessing XML documents. In this paper we propose a new approach to clustering XML data. In contrast to previous work, which focused on documents defined by different DTDs, the proposed method works for documents with the same DTD. Our approach is to extract features from documents, modeled by ordered labeled trees, and transform the documents to vectors in a high-dimensional Euclidean space based on the occurrences of the features in the documents. We then reduce the dimensionality of the vectors by principal component analysis (PCA) and cluster the vectors in the reduced dimensional space. The PCA enables one to identify vectors with co-occurrent features, thereby enhancing the accuracy of the clustering. We also discuss an extension of our techniques to XML retrieval. Experimental results based on documents obtained from Wisconsin's XML data bank show the effectiveness and good performance of the proposed techniques.
34826531
User-assisted query translation for interactive CLIR
We view interactive Cross-Language Information Retrieval (CLIR) as an iterative process in which the searcher and the retrieval system collaborate to find documents that satisfy the searcher’s needs, regardless of the language in which those documents are written. Our motivation is that humans and machines can bring complementary strengths to this process. Machines are excellent at repetitive tasks that are well specified; humans bring creativity and exceptional pattern recognition capabilities. Properly coupling these capabilities can result in a synergy that greatly exceeds the ability of either human or machine alone. Designers of CLIR systems can select from a variety of fully automatic techniques to overcome problems with unknown terms and translation ambiguity, but automatic processing of this sort risks reducing the searcher’s understanding of system operation. This, in turn, tends to work against the synergy that we seek to accomplish. We are therefore exploring more transparent approaches to support interactive CLIR.
28415004
Scientific Data Mining: A Case Study
Scientific data mining is the activity of finding significant information in scientific data. This paper presents an example of scientific data mining: the discovery of approximately common patterns in RNA secondary structures. We represent an RNA secondary structure by an ordered labeled tree based on a previously proposed scheme. The patterns in the trees are substructures that can differ in both substitutions and deletions/insertions of nodes of the trees. Our techniques incorporate approximate tree matching algorithms and novel heuristics for discovery and optimization. Experimental results obtained by running these algorithms on both generated data and RNA secondary structures show the good performance of the algorithms. It is shown that the optimization heuristics speed up the discovery algorithm by a factor of 10. Moreover, our optimized approach is 100,000 times faster than the brute force method.
19002258
Reconstructing manifold and non-manifold surfaces from point clouds
This paper presents a novel approach for surface reconstruction from point clouds. The proposed technique is general in the sense that it naturally handles both manifold and non-manifold surfaces, providing a consistent way for reconstructing closed surfaces as well as surfaces with boundaries. It is also robust in the presence of noise, irregular sampling and surface gaps. Furthermore, it is fast, parallelizable and easy to implement because it is based on simple local operations. In this approach, surface reconstruction consists of three major steps: first, the space containing the point cloud is subdivided, creating a voxel representation. Then, a voxel surface is computed using gap filling and topological thinning operations. Finally, the resulting voxel surface is converted into a polygonal mesh. We demonstrate the effectiveness of our approach by reconstructing polygonal models from range scans of real objects as well as from synthetic data.