Automatic filter selection using image quality assessment

Souza, Alex Júnior Souza de; Cheriet, Mohamed; Naoi, Satoshi; Suen, Ching Y.

doi:10.1109/icdar.2003.1227717

Cited by 22 publications

(22 citation statements)

References 6 publications

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“…Small Speckle Factor (SSF) [13]: This is similar to SSF defined in [9], but counts the number of connected components with fewer than O.S*FS pixels.…”

Section: Quality Metricsmentioning

confidence: 99%

“…Most are defined to quantitatively capture the different types of degradations seen in large document collections. Eight different quality measures defined in [7,9,10,13] were implemented for these experiments. Some quality metrics aim to measure the same effect, but the method used to calculate the degradation effect has been defined differently by different authors.…”

Section: Quality Metricsmentioning

confidence: 99%

“…Stroke Thickness Factor (STF) [13]: This is the most frequently measured thickness in the horizontal direction. At each row of the text lines, the number of consecutive black pixels is counted.…”

Section: Quality Metricsmentioning

confidence: 99%

“…Touching Character Factor (TCF) [9,13]: The TCF measures the degree to which neighboring characters touch. This is looking for long and low connected components.…”

Section: Quality Metricsmentioning

confidence: 99%

“…These methods utilized quality metrics calibrated from the image scanned for OCR. More recently an approach has surfaced where authors are designing systems to automatically choose a filter that when applied to the degraded document image improves the image quality and therefore the OCR performance [9,13,14]. Here the choice offilter is related to the level and type of degradation present in the document image.…”

Section: Introductionmentioning

confidence: 99%

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<title>Correlating degradation models and image quality metrics</title>

Reed

Smith

2008

SPIE Proceedings

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OCR often performs poorly on degraded documents. One approach to improving performance is to determine a good filter to improve the appearance of the document image before sending it to the OCR engine. Quality metrics have been measured in document images to determine what type of filtering would most likely improve the OCR response for that document image. In this paper those same quality metrics are measured for several word images degraded by known parameters in a document degradation model. The correlation between the degradation model parameters and the quality metrics is measured. High correlations do appear in many places that were expected. They are also absent in some expected places and offer a comparison of quality metric definitions proposed by different authors.

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“…Small Speckle Factor (SSF) [13]: This is similar to SSF defined in [9], but counts the number of connected components with fewer than O.S*FS pixels.…”

Section: Quality Metricsmentioning

confidence: 99%

Section: Quality Metricsmentioning

confidence: 99%

Section: Quality Metricsmentioning

confidence: 99%

“…Touching Character Factor (TCF) [9,13]: The TCF measures the degree to which neighboring characters touch. This is looking for long and low connected components.…”

Section: Quality Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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