Paper
7 March 1996 Assessment of image quality to predict readability of documents
Author Affiliations +
Proceedings Volume 2660, Document Recognition III; (1996) https://doi.org/10.1117/12.234714
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
Determining the readability of documents is an important task. Human readability pertains to the scenario when a document image is ultimately presented to a human to read. Machine readability pertains to the scenario when the document is subjected to an OCR process. In either case, poor image quality might render a document un-readable. A document image which is human readable is often not machine readable. It is often advisable to filter out documents of poor image quality before sending them to either machine or human for reading. This paper is about the design of such a filter. We describe various factors which affect document image quality and the accuracy of predicting the extent of human and machine readability possible using metrics based on document image quality. We illustrate the interdependence of image quality measurement and enhancement by means of two applications that have been implemented: (1) reading handwritten addresses on mailpieces and (2) reading handwritten U.S. Census forms. We also illustrate the degradation of OCR performance as a function of image quality. On an experimental test set of 517 document images, the image quality metric (measuring fragmentation due to poor binarization) correctly predicted 90% of the time that certain documents were of poor quality (fragmented characters) and hence not machine readable.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Venu Govindaraju and Sargur N. Srihari "Assessment of image quality to predict readability of documents", Proc. SPIE 2660, Document Recognition III, (7 March 1996); https://doi.org/10.1117/12.234714
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Image quality

Image enhancement

Optical character recognition

Quality measurement

Image processing

Scanners

Detection and tracking algorithms

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