OCR andSALIX Parsing

Daryl LaffertyArizona 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

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

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

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

The SALIX User Interface

Manual Data Entry

A label that results in many OCR errors

A label that results in few OCR errors

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.

Sample Throughput Data

Conclusions

S=4

√E

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

andE = 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)

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.