His primary scientific interests are in Information retrieval, Artificial intelligence, Optical character recognition, Decision table and Computer vision. His Information retrieval research includes themes of Data mining, Encoding and Cluster analysis. In most of his Artificial intelligence studies, his work intersects topics such as Line.
The study incorporates disciplines such as Document recognition and Pattern recognition in addition to Optical character recognition. His research in Computer vision intersects with topics in Character, Process and Error detection and correction. His studies deal with areas such as Computer file, Connected component, Font and Pattern recognition as well as Image.
Daniel P. Lopresti mostly deals with Artificial intelligence, Pattern recognition, Information retrieval, Optical character recognition and Image. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Computer vision and Natural language processing. His biological study spans a wide range of topics, including Process, Decoding methods and Error detection and correction.
His research integrates issues of Document layout analysis, Data mining and Database in his study of Information retrieval. Daniel P. Lopresti has included themes like Algorithm, Approximate string matching, Character and Pattern recognition in his Optical character recognition study. His Handwriting recognition study also includes
Daniel P. Lopresti mainly focuses on Artificial intelligence, Pattern recognition, Image, Psychological resilience and Pixel. His Artificial intelligence research focuses on Deep learning, Artificial neural network, Robustness, Pattern recognition and Attention network. His work carried out in the field of Pattern recognition brings together such families of science as Speech recognition, Document processing, Word error rate, Preprocessor and Noise.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Contextual image classification and Benchmark. His Image study incorporates themes from Range, Ground truth and Open research. Daniel P. Lopresti has researched Pixel in several fields, including Histogram and Graph.
The scientist’s investigation covers issues in Computer security, Image, Internet privacy, Internet of Things and Data mining. His work on Data security as part of general Computer security study is frequently connected to Economic impact analysis, Natural resource and Urbanization, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. The Image study combines topics in areas such as Range, Ground truth and Open research.
His Data mining research is multidisciplinary, incorporating elements of Handwriting recognition, Benchmarking, Linear regression and Handwriting. His study on Preprocessor is covered under Artificial intelligence. His Artificial intelligence study often links to related topics such as Speech recognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Document search and retrieval system with partial match searching of user-drawn annotations
Lopresti Daniel P;Yue Ma;Zhou Jian.
Building and using a highly parallel programmable logic array
M. Gokhale;W. Holmes;A. Kopser;S. Lucas.
IEEE Computer (1991)
Video user's environment
Daniel P Lopresti;Yue Ma;Andrew Tomkins;Jian Zhou.
Caption and photo extraction from scanned document images
Zhou Jiangying;Lopresti Daniel P.
Method and apparatus for filtering e-mail infected with a previously unidentified computer virus
Michael R. Andrews;Gregory P. Kochanski;Daniel Philip Lopresti;Chi-Lin Shih.
Table-processing paradigms : a research survey
David W. Embley;Matthew Hurst;Daniel P. Lopresti;George Nagy.
International Journal on Document Analysis and Recognition (2006)
Method and means for enhancing optical character recognition of printed documents
Danieru Pii Ropuresutei;Jiyonasan Esu Sandobaagu.
Clock free two-dimensional barcode and method for printing and reading the same
Daniel P. Lopresti;Jeffrey Esakov;Jiangying Zhou.
Block edit models for approximate string matching
Daniel Lopresti;Andrew Tomkins.
Theoretical Computer Science (1997)
System and method for archiving digital versions of documents and for generating quality printed documents therefrom
Daniel Lopresti;Jeffrey Esakov;Jiangying Zhou.
International Journal on Document Analysis and Recognition
(Impact Factor: 3.87)
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