NDLI: Extracting mathematical expressions from postscript documents

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Extracting mathematical expressions from postscript documents

Content Provider	ACM Digital Library
Author	Fateman, Richard Yang, Michael
Abstract	Full-text indexing of documents containing mathematics cannot be considered a complete success unless the mathematics symbolism is extracted and represented in a standardized form permitting both searching for formulas, and re-use of this information in (for example) computer algebra systems. Most documents produced in the past and subsequently digitally encoded, and even most of those potentially "born digital" in current journal production are---at best---encoded in a printer form such as Adobe Postscript [1], in which mathematics is not explicitly marked or easily identifiable. While one might look forward in the future to other document encodings such as MathML, the common journal or textbook product is essentially without semantic content: a jumble of odd characters. Sometimes it is just a jumble of black and white dots! In this paper we demonstrate an approach to decoding, to recognizing and extracting mathematical expressions, from a Postscript document. We can produce a syntactic representation of the extracted expressions which can then be used to generate various forms. For example, if we extract TeX or Presentation MathML, we can re-typeset the expression, but perhaps in a different size or font family. More significantly, if we start from this presentation information, we can hope to combine it with additional contextual processing of the surrounding text and meta-data associated with the document, to assign semantics,(e.g. content MathML), or provide versions in computer algebra system languages such as Maple or Mathematica. Finally, it is possible to use this material to present audio or braille versions of mathematics for the visually disabled. We have previously addressed some aspects of the higher level of processing (parsing TeX for example). In this paper we address the only first stage and concentrate on what may seem to be overly simple, but is in fact difficult to do precisely: extracting the mathematics parts from text.
Starting Page	305
Ending Page	311
Page Count	7
File Format	PDF
ISBN	158113827X
DOI	10.1145/1005285.1005329
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-07-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Optical character recognition Digital library Document image analysis Postscript documents Mathematics
Content Type	Text
Resource Type	Article

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