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Get a Grip – How to Handle OCR Failure

Optical Character Recognition (OCR) technology may seem like the end-all, be-all solution for scanning and capturing accurate data. And while OCR does have its benefits, it can be frustrating when data is misread or not read at all – forcing knowledge workers to manually work items.

With the help of our customers, we’ve identified a few culprits that cause OCR to fail. Now – how do we handle them?

CulpritHow to Handle
Poor image qualityRequest image quality be improved at the source; ask customers and/or banks to provide you better image quality.
Poor scanning resolutionScan images in 300 dpi or greater.The recommended best scanning resolution for OCR accuracy is 300 dpi. Higher resolutions do not necessarily result in better accuracy and can slow down OCR processing time. Resolutions below 300 dpi may affect the quality and accuracy of OCR results.
Handwritten documentsAvoid handwritten documents.Handwritten documents can be expected to produce subpar OCR results, and it is likely to result in the need to manually key data
Low-contrast documentsRequest higher-contrast documents.Low-contrast documents can result in poor OCR. For text that has poor readability, some pre-processing may be required. Request that customers increase the contrast to make characters more distinct. The following may also improve contrast: (1) removal of text lines by deskewing and dewarping text, (2) illumination of image (e.g. eliminating dark part of image), and (3) de-noising the image.
Inconsistent use of font faces and sizesRequest customers use clear font faces and sizes. Font sizes of below 6 points can limit OCR accuracy. Try to avoid dot matrix printers and unreadable fonts.
Poorly maintained scanning equipmentPerform scheduled scanner maintenance. Dirty scanners can produce bad quality images. If you are scanning in-house or your bank is scanning documents, make sure regular maintenance is followed.
Inefficient scan processPerform ongoing training and review of workers. Teach proper technique when workers load documents into the scanner (e.g. not skewed). Proper alignment of the initial scan can affect OCR quality; crooked lines of text produce poor results.

Share your comments below.

What other culprits have you identified causing OCR failure?

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