A system and method are disclosed for extracting information from text which can be performed without prior knowledge as to whether the text includes a list. The method applies parser rules to a sentence spanning lines of text to identify a set of candidate list items in the sentence. Each candidate list item is assigned a set of features including one or more non-linguistic feature and a linguistic feature. The linguistic feature defines a syntactic function of an element of the candidate list item that is able to be in a dependency relation with an element of an identified candidate list introducer in the same sentence. When two or more candidate list items are found with compatible sets of features, a list is generated which links these as list items of a common list introducer. Dependency relations are extracted between the list introducer and list items and information based on the extracted dependency relations is output.

Parsing of text using linguistic and non-linguistic list properties

Scanned then OCRed documents usually lack detailed layout and structural information. We present a book specific layout analysis system used to extract TOC structure information from the scanned and OCRed books. This system was used for navigation purposes by the live books search project. We provide labeling scheme for the TOC sections of the books, high level overview for the book layout analysis system, as well as TOC Structure Extraction Engine. In the end we present accuracy measurements of this system on a representative test set.

Book Layout Analysis: TOC Structure Extraction Engine

A system and method for determining hierarchical information is described. Aspects include using the Collins model for parsing non-textual information into hierarchical content. The system and process assign labels to lines that indicate how the lines relate to one another.

Parsing hierarchical lists and outlines

Handwritten text lines are prominent structures in freeform digital ink notes and their reliable detection is the foundation to a natural and intelligent interface for note editing and repurposing. This paper presents an optimization method for text line grouping. The global'cost function is designed to find the simplest stroke partitioning to maximize the likelihood of the resulting lines and the consistency of their configuration. A dynamic programming algorithm provides an initial segmentation of the time-ordered stroke sequence. Then a local gradient-descent algorithm iteratively evaluates splitting and merging hypotheses to minimize the global cost function. On average, the proposed technique processes each note page in less than a second at 90% accuracy.

Grouping text lines in freeform handwritten notes

Annotation is an integral part of reading, comprehending, commenting, and authoring notes and documents. In this paper we present a system for recognizing annotations in a flexible digital notebook that may contain a variety of content ranging from text, to images, to handwritten notes. To accomplish the recognition task in real-time makes the complicated annotation parsing problem more difficult.Our approach differs from previous approaches in several ways. First, our approach handles annotations on ink notes, which are significantly more ambiguous than annotations on printed documents and hence more difficult to recognize. Second, our approach is entirely learned from data, so it is easy to adapt to other scenarios. Third, our approach is more thoroughly evaluated than previous systems. On a test set of real user notes, the system has achieved an average recall of 0.9258 on all annotation types. Finally, the implementation of the approach will be commercially available as an API in the upcoming release of Windows® Vista® and Office 12®.

Parsing ink annotations on heterogeneous documents

Handwritten notes are complex structures, which include blocks of text, drawings, and annotations. The main challenge for the newly emerging tablet computer is to provide high-level tools for editing and authoring handwritten documents using a natural interface. One frequent component of natural notes are lists and hierarchical outlines, which correspond directly to the bulleted lists and itemized structures in conventional text, editing tools. We present a system, which automatically recognizes lists and hierarchical outlines in handwritten notes, and then computes the correct structure. This inferred structure provides the foundation for new user interfaces and facilitates the importation of handwritten notes into conventional editing tools.

http://rdg.ext.hitachi.co.jp/iwfhr9/AfterWS/IWFHR9-Proceedings/docs/026_x_ye-LearningtoParse.pdf

Learning to parse hierarchical lists and outlines using conditional random fields

Parsen von hierarchischen Listen und Übersichten

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Learning to parse hierarchical lists and outlines using conditional random fields

Parsen von hierarchischen Listen und Übersichten