Notes on Contemporary Table Recognition

Embley, David W.; Lopresti, Daniel; Nagy, George

doi:10.1007/11669487_15

Cited by 27 publications

(14 citation statements)

References 9 publications

(9 reference statements)

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“…Anomalous word-frequency profile checks (like "spare" appearing too often in a document that has an above average number of occurrences of "spore") may be useful as well. Support for the logical markup of tables can be had by integrating a tool we built previously for that purpose [4].…”

Section: Prototypementioning

confidence: 99%

Tools for monitoring, visualizing, and refining collections of noisy documents

Lopresti

Nagy

2009

Proceedings of the Third Workshop on Analytics for Noisy Unstructured Text Data

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Developing better systems for document image analysis requires understanding errors, their sources, and their effects. The interactions between various processing steps are complex, with details that can be obscured by the statistical methods that are employed in many cases. In this paper, we describe tools we are building to help the user view and understand the results of common document analysis procedures. Unlike existing platforms for ground-truthing page images, our system also allows users to visualize the results of automated error analyses. Recognition errors can be corrected interactively, with the effort to do so recorded as a measure that is useful in performance evaluation.Beyond this functionality for exploring error behavior, we consider how such tools could be designed to improve the quality of collections of badly recognized documents incrementally as users interact with them on a regular basis. We conclude by discussing topics for future research.

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Section: Prototypementioning

confidence: 99%

Tools for monitoring, visualizing, and refining collections of noisy documents

Lopresti

Nagy

2009

Proceedings of the Third Workshop on Analytics for Noisy Unstructured Text Data

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“…Suppose, for instance, that we process the left-hand table in Figure 8 and include it into the ontology. Then when we encounter the right-hand table we hope to be able to learn that the hepth of goldam is 320 gd [26]. Our current plans to build interactive software for harvesting web tables based on the formalisms described above are outlined in Section 5.…”

Section: Labeled Table Candidates For Which Wang Notation Exists Are mentioning

confidence: 99%

From Tessellations to Table Interpretation

Jandhyala

Krishnamoorthy

Nagy

et al. 2009

Lecture Notes in Computer Science

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Abstract. The extraction of the relations of nested table headers to content cells is automated with a view to constructing narrow domain ontologies of semistructured web data. A taxonomy of tessellations for displaying tabular data is developed. X-Y tessellations that can be obtained by a divide-and-conquer method are asymptotically only an infinitesimal fraction of all partitions of a rectangle into rectangles. Admissible tessellations are the even smaller subset of all partitions that correspond to the structures of published tables and that contain only rectangles produced by successive guillotine cuts. Many of these can be processed automatically. Their structures can be conveniently represented by X-Y trees, which facilitate relating hierarchical row and column headings to content cells. A formal grammar is proposed for characterizing the X-Y trees of layout-equivalent admissible tessellations. Algorithms are presented for transforming a tessellation into an X-Y tree and hence into multidimensional, layoutindependent Category Trees (Wang abstract data types).

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“…Again, we would like a format that allows a smooth transition to a database query language. We favor Wang Notation, which provides a layout-independent representation of the relations between hierarchical category headers and content cells [149,150]. For archival circuit diagrams and engineering drawings, the natural choice seems to be one of the widespread CAD formats (like Spice, Synopsis, and AutoCad).…”

Section: Interoperabilitymentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

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Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

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Notes on Contemporary Table Recognition

Cited by 27 publications

References 9 publications

Tools for monitoring, visualizing, and refining collections of noisy documents

Tools for monitoring, visualizing, and refining collections of noisy documents

From Tessellations to Table Interpretation

Adaptive and Interactive Approaches to Document Analysis

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