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OCR and SALIX Parsing

OCR and SALIX Parsing. Daryl Lafferty Arizona State University October, 2012. SALIX: Semi-Automatic Label Information eXtraction. SALIX was developed at Arizona State University from 2009 through 2012. Over 55,000 ASU Herbarium specimen labels were digitized using SALIX.

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OCR and SALIX Parsing

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  1. OCR andSALIX Parsing Daryl Lafferty Arizona State University October, 2012

  2. SALIX:Semi-Automatic Label Information eXtraction SALIX was developed at Arizona State University from 2009 through 2012. Over 55,000 ASU Herbarium specimen labels were digitized using SALIX

  3. Ideal SALIX Process Flow The ideal process flow is: Photograph the specimen label Perform OCR on the photograph Have SALIX parse the resulting text into database categories Upload the results to the database

  4. Practical SALIX Process Flow The actual process flow has added steps: Photograph the specimen label Perform OCR on the photograph Correct any OCR errors. Tweak the text layout Have SALIX parse the resulting text into database categories Correct any mis-parsed results Upload the results to the database

  5. OCR Workflow We use a ABBYY Professional Version 10 We capture an image of the full specimen, and another of just the label for OCR. Processing is done in batch mode, usually run over night on a folder containing hundreds of images. The result is a single text file with one label per page. OCR errors are corrected in the text file before processing with SALIX

  6. The SALIX User Interface

  7. Manual Data Entry

  8. A label that results in many OCR errors

  9. A label that results in few OCR errors

  10. Label Length and Quality • We first categorized 4 different label types, with the following average characteristics: • We then had 3 students each process 10 labels of each category (40 labels total through SALIX and typed into Symbiota form.

  11. Sample Throughput Data

  12. Conclusions OCR quality has a strong effect on semi-automated parsing throughput using SALIX. OCR using ABBYY in Batch Mode was most efficient for our workflow. The relationship is roughly: where S = Ratio of SALIX Throughput/Typing Throughput and E = OCR Error rate stated as OCR Errors per 100 words (Obviously, the relationship isn't accurate as E approaches zero, i.e. less than about 2 Errors/100 words)

  13. Acknowledgements All of the data presented here was from Anne Barber's Master's Thesis, completed at ASU in May, 2012. Anne also developed the process flow that helped optimize SALIX throughput. The overall project was under the direction of Les Landrum, curator of the ASU Herbarium.

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