Catherine N. Ball's scientific contributions

Converting files to Portable Document Format (PDF) is popular due to the format's many advantages. For example, PDF allows an author to control or preserve the rendering of a digital document, distribute it to other systems, and ensure that it displays in a viewer as intended. From the perspective of Human Language Technology (HLT), however, PDFs are problematic. PDF is a display-oriented digital document format; the point of PDF is to preserve the appearance of a document, not to preserve the original electronic text. We observed errors in PDF-extracted text indicating that either the PDF generator or extractor, or both, mishandled the document structure, character data, and/or entire textual objects. And we learned that other HLT researchers reported data loss when extracting electronic text from PDFs. This motivated further study of digital document data exchange using PDFs. MITRE conducted an exploratory study of data exchange using PDF in order to investigate the data loss phenomenon. We limited our study to Middle Eastern electronic text: specifically Arabic and Persian. The study included a test for scoring PDF generation methods?(a) using a common, best-practice setup to generate PDFs and extract text, and (b) using character accuracy to quantify the quality of PDF-extracted text. We ranked 8 methods according to the resulting accuracy scores. The 8 methods map to 3 core PDF generation classes. At best, the Microsoft Word class resulted in 42% Overall Accuracy. Best scores for the PDFMaker and Acrobat Distiller/PScript5.dll classes were 95% and 96%, respectively. This paper explains our tests and discusses the results, including evidence that using PDF for data exchange of typical Arabic and Persian documents results in a loss of important electronic text content. This loss confuses human language technologies such as search engines, machine translati

Electronic text for use by human language technologies originates from a number of sources direct keyboard entry, optical character recognition, speech recognition, and text-containing computer files. In particular, text-containing computer files may elude processing by an array of human language technology applications (e.g., search, language ID, machine translation, and text analytics). This paper brings to light the effort required to extract electronic text from these files preserve its integrity, and, for some use cases, preserve its structure. It explores a series of specific human language technologies, highlighting the following aspects for each: relevant use cases, the impact of text extraction or conversion errors, the criticality of dependable text extraction and reliable electronic text, and the importance of experimentation and/or testing prior to use. Overall, this paper promotes the successful use of human language technology by equipping the reader to be discerning about the use of human language technology applications with text-containing files.

This paper describes a methodology for end-to-end evaluation of Arabic document image processing software. The methodology can be easily tailored to other languages, and to other document formats (e.g., audio and video). Real-world documents often involve complexities such as multiple languages, handwriting, logos, signatures, pictures, and noise introduced by document aging, reproduction, or exposure to environment factors. Information retrieval systems that implement algorithms to account for such factors are maturing. The proposed methodology is vital for measuring system performance and comparing relative merits.