BL Demo Day - July2011 - (3) Image Enhancement for OCR
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Slides from Niall Anderson's 1st presentation on Image Enhancement and OCR at the British Library Demo-day on the 12th July 2011.
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BL Demo Day - July2011 - (3) Image Enhancement for OCR
- 1. Image Enhancement and OCR Niall Anderson, The British Library, 12 July 2010
- 3. Example of an enhanced image Warped Dewarped
- 6. IMPACT Image Enhancement toolkit
- 18. Indicative results – Border Removal 22383 images to test border removal BL: 7% BNE: 34% BNF: 34% BSB: 11% JSI: 6% NLB: 2% ONB: 6% Only images with borders 38718 images to test border removal BL: 9% BNE: 29% BNF: 32% BSB: 12% JSI: 11% NLB: 2% ONB: 5%
- 19. Indicative results – Page splitting 458 images from BNF to test page split 3009 images to test page split BL: 72% BSB: 10% JSI: 18%
- 20. Indicative results - Dewarping IMPACT Page Curl Correction v.4 87.78% (81.98% only coarse correction) BookRestorer 80.87%
Editor's Notes
- While generally used in the pre-processing stage of OCR production, Image Enhancement techniques can, in theory, be used on any digital image, created for whatever purpose. Legacy images – not created with OCR in mind – can also benefit from this approach.
- Created by IMPACT partner agency NCSR “Demokritos” in Athens
- Done automatically based on projection profiles and zonal analysis. Can also be resized manually as in this example.
- Extension of process used in border removal. Can also be resized manually.
- If automated processing works, why manual intervention? Use in collection triage: finding the best parameters before submitting images to OCR.
- Items are selected and deselected by clicking on the icons. The workflow parts can also be swapped around for best results