Impact Analysis of OCR Quality on Research Tasks in Digital ArchivesMyriam C. Traub, Jacco van Ossenbruggen, Lynda HardmanCentrum Wiskunde & Informatica, Amsterdam

Context

✤ Research in collaboration with the National Library of The Netherlands

✤ Digital newspaper archive:

✤ 10 million pages covering 1618 to 1995

✤ +/- 1200 newspaper titles

✤ Available data: scanned image of the page, OCRed text and metadata records

2

Interviews

✤ Aim:

✤ Find out what types of research tasks scholars perform on digital archives

✤ Which quantitative / distant reading tasks are not (sufficiently) supported

✤ Scholars with experience in performing historical research on digital archives

3

Categorization of research tasks

T1 find the first mention of a concept

T2 find a subset with relevant documents

T3 investigate quantitative results over time

T3.a compare quantitative results for two terms

T3.b compare quantitative results from two corpora

T4 tasks using external tools on archive data

5

I mostly use digital archives for exploration of a topic, selecting

material for close reading (T1, T2) or external processing (T4).

OCR quality in digital archives / libraries is partly very bad.

I cannot quantify its impact on my research tasks.

I would not trust quantitative analyses (T3a, T3b) based on this data sufficiently to use it in publications.

Literature

✤ OCR quality is addressed from the perspective of the collection owner/OCR software developer

✤ Usability studies for digital libraries

✤ Robustness of search engines towards OCR errors

✤ Error removal in post-processing either systematically or intellectually

6

We care about average

performance on representative subsets

for generic cases.

I care about actual performance

on my non-representative subset

for my specific query.

7

Two different perspectives of quality evaluation

Use case

✤ Aims:

✤ To study the impact on research tasks in detail

✤ Identify starting points for workarounds and/or further research

✤ Tasks T1 - T3

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T1: Finding the first mention

✤ Key requirement: recall

✤ 100% recall is unrealistic

✤ Aim: Find out how a scholar can assess the reliability of results

9

“Amsterdam”

1642

10

First mention of …

… in the OCRed newspaper archive of the KB?

1618

earliest document

OCR

pre-processing

post-

processing

ingestion

scanning

11

Understanding potential sources of bias and errors

✤ many details difficult to reconstruct

✤ essential to understand overall impact

“Amsterdam”

1642

12

First mention of …

… in the OCRed newspaper archive of the KB?

1618

earliest document

“Amfterdam”

1624

01

OCR confidence values useful?

✤ Available for all items in the collection: page, word, character

✤ Only for highest ranked words / characters, other candidates missing

✤ This information would be required to estimate recall.

13

Confusion table

✤ Applied frequent OCR confusions to query

✤ 23 alternative spellings, but none of them yielded an earlier mention

✤ Problem: long tail

Amstcrdam 16-01-1743 Amstordam 01-08-1772 Amsttrdam 04-08-1705 Amslerdam 12-12-1673 Amslcrdam 20-06-1797 Amslordam 29-06-1813 Amsltrdam 13-04-1810 Amscerdam 17-10-1753 Amsccrdam 16-02-1816 Amscordam 01-11-1813 Amsctrdam 16-06-1823 Amfterdam already found Amftcrdam 17-08-1644 Amftordam 31-01-1749 Amfttrdam 26-11-1675 Amflerdam 03-03-1629 Amflcrdam 01-03-1663 Amflordam 05-03-1723 Amfltrdam 01-09-1672 Amfcerdam 22-04-1700 Amfccrdam 27-11-1742 Amfcordam - Amfctrdam 09-10-1880

correct confused

s f

n u

e c

n a

t l

t c

h b

l i

e o

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full table available online:http://persistent-identifier.org/?identifier=urn:nbn:nl:ui:18-23429

“Amsterdam”

1642

“Amfterdam”

1624

“Amsterstam”

1619

15

First mention of …

1618

… in the OCRed newspaper archive of the KB?

earliest document

“Amsterdam”

1642

“Amfterdam”

1624

“Amsterstam”

1619

16

Update!

1618

Corrections for 17th century newspapers were crowdsourced!

earliest document

“Amsterdam”

1620

… but why not 1619?

Confusion Matrix OCR Confidence Values

Alternative Confidence Values

available: sample only full corpus not available

T1 find all queries for x, impractical

estimated precision, not helpful

improve recall

T2 as above estimated precision, requires improved UI

improve recall

T3 pattern summarized over set of alternative queries

estimates of corrected precision

estimates of corrected recall

T3.a warn for different susceptibility to errors

as above, warn for different distribution of confidence values

as above

T3.b as above as above as above

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No silver bullet

✤ we propose novel strategies that solve part of the problem:✤ critical attitude (awareness and better support)

✤ transparency (provenance, open source, documentation, …)

✤ alternative quality metrics(taking research context into account)

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Conclusions

Problems

✤ Scholars see OCR quality as a serious problem, but cannot assess its impact

✤ OCR technology is unlikely to be perfect

✤ OCR errors are reported in terms of averages measured over representative samples

✤ Impact on a specific research task cannot be assessed based on average error metrics

Start of solutions

✤ Impact of OCR is different for different research tasks, so these tasks need to made be explicit

✤ OCR errors often assumed to be random but are often partly systematic

✤ Tool pipelines and their limitations need to be transparent & better documented

Translate the established tradition of source criticism to the digital world and create a new tradition of tool criticism to systematically identify and explain technology-induced bias.

#toolcrit21