Abstract
The purpose of this research is to reverse engineer the process of encoding data in structured documents and subsequently automate the process of extracting it. We assume a broad category of structured documents for processing that goes beyond form processing. In fact, the documents may have flexible layouts and consist of multiple and varying numbers of pages. The data extraction method (DataX) employs general templates generated by the Inductive Template Generator (InTeGen). The InTeGen method utilizes inductive learning from examples of documents with identified data elements. Both methods achieve high automation with minimal user’s input.
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Keywords
- Principle Component Analysis
- Relevant Document
- Document Image
- Document Representation
- Original Feature Space
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Wnek, J. (2002). Machine Learning of Generalized Document Templates for Data Extraction. In: Lopresti, D., Hu, J., Kashi, R. (eds) Document Analysis Systems V. DAS 2002. Lecture Notes in Computer Science, vol 2423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45869-7_48
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DOI: https://doi.org/10.1007/3-540-45869-7_48
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