A platform for storing, visualizing, and interpreting collections of noisy documents

The goal of document image analysis is to produce interpretations that match those of a uent and knowledgeable human when viewing the same input. Because computer vision techniques are not perfect, the text that results when processing scanned pages is frequently noisy. Building on previous work, we propose a new paradigm for handling the inevitable incomplete, partial, erroneous, or slightly orthogonal interpretations that commonly arise in document datasets. Starting from the observation that interpretations are dependent on application context or user viewpoint, we describe a platform now under development that is capable of managing multiple interpretations for a document and oers an unprecedented level of interaction so that users can freely build upon, extend, or correct existing interpreta c ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The denitive version was published in the ACM...

• Type: Journal
• Year: 2010
• Where: AND
• Authors: Bart Lamiroy, Daniel P. Lopresti
• Keywords: AND 2010, Document, Document Image Analysis, Interpretations, Machine Learning