Digitisation Doctor Optical Character Recognition

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Simon Tanner

Optical Character Recognition guidance and advice

Technology Business

Simon Tanner
Blog: simon-tanner.blogspot.co.uk
Twitter: @SimonTanner

www.slideshare.net/KDCS/

King’s Digital Consultancy Services

www.digitalconsultancy.net

Deciding whether Optical Character Recognition is feasible
(PDF document) created for the Oxford University Digital
Library
www.odl.ox.ac.uk/papers/OCRFeasibility_final.pdf

Measuring Mass Text Digitization Quality and Usefulness:
Lessons Learned from Assessing the OCR Accuracy of the
British Library's 19th Century Online Newspaper Archive
www.dlib.org/dlib/july09/munoz/07munoz.html

www.impact-project.eu

Uniformity
Language
Text alignment
Complexity of alignment
Lines, graphics and pictures
Handwriting

Evaluating OCR accuracy is about more than just
character to character accuracy rates
Character accuracy rates are misleading
(more later…)
It is also about assessing the functionality enabled
through the OCR’s output
Search accuracy
Volume of hits returned
Ability to structure searches and results
Accuracy of result ranking
Amount of correction required to
achieve the required performance

Consider this scenario:
1,000 words with 5,000 characters
(an average of 5 per word) excluding spaces

90% character accuracy means:
4,500 characters correct
Possibly a maximum 900 words correct (90%)
Possibly a minimum 500 words correct (50%)
Reality is somewhere in between
Depending on the number of “significant
words” the search results could still
be almost 100% or near zero

100

90

80

70

60

50
1801

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900
characters words
words with capital letter start significant words
Poly. (characters) Poly. (words)
Poly. (significant words) Poly. (words with capital letter start

OCR Results
% characters % words correct No. of corrections
OCR Engine correct
FineReader 91.1 70.9 110
PrimeOCR 93.95 79.1 79

Total number of characters = 2109
Total number of words = 379

I am petfood, God toil! uttedy-toverthroW, at feaft; $gy abafe
Men's affections tp; and seal for all Party-making Notions
amdngft CfiriftiansybefGieirie will raife his,Church to that prof-
perous, flourilhing State prophefied of, and prOmifed in the
Scrip* tures. There mult be more Love, and Charity, and
Unanimity amongft Chriftians,.

OCR Results

% characters % words correct No. of corrections
OCR Engine correct
FineReader 73.7 57.5 31
PrimeOCR 75.9 62.37 28

Total number of characters = 411
Total number of words = 73

A THEATRE
erein be reprc-fented as wel the miferies &
calamities tijat foiioto tht too*
e^jr alfo the greate toyts and
plefures tobtcf) tbe fatrfc faltooenio^
An Argument both profitable and
dele&able, to all that finccrcly
loue the word of Codt'.
*Deuifedby S. hhnv&n~ derlS^oodt.
s 3^ Scene and allowed according to the order
appointed.
, ^ Imprinted at London by Henry Bynncman*
Anno Domini.
CVM PHIT

Simon Tanner
Blog: simon-tanner.blogspot.co.uk
Twitter: @SimonTanner

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A study that investigated first-year undergraduate student use of an Optical Character Recognition (OCR) mobile application designed to help students find resources for course assignments. The app uses textual content from the assignment sheet to suggest relevant library resources which students may not be aware. The study methodology used formative evaluation techniques; data were collected to inform the production level version of the mobile application and to understand student use models and requirements for OCR software in mobile applications.

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Optical Character Recognition (OCR) is the process which enables a system to without human intervention identifies the scripts or alphabets written into the users’ verbal communication. Optical Character identification has grown to be individual of the mainly flourishing applications of knowledge in the field of pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this paper we resolve and examine the hypothetical and numerical models of Optical Character Identification. The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR) techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on Optical Character detection or recognition has shown that the In Handwritten text there is no limitation lying on the script technique. Hand written correspondence is complicated to be familiar through due to diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of approaches of Optical Character Identification is discussed here all along through their achievement.

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