Kanton Zürich Direktion der Justiz und des Innern Staatsarchiv. Project. Automatisierte Handschriftenerkennung. Tobias Hodel (Staatsarchiv Zürich)

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1 Kanton Zürich Direktion der Justiz Staatsarchiv Project Automatisierte Handschriftenerkennung Tobias Hodel (Staatsarchiv Zürich)

2 - Making archival (esp. handwritten) documents better accessible - New research infrastructure Transkribus - Funded until mid-2019 by the European Union - 14 European partners Direktion der Justiz What is READ Recognition and Enrichment of Archival Documents

3 Talking points - How is text recognized - Automated text recognition with neural networks - Quality of recognition (amount of Ground Truth) - Transkribus platform and interfaces (WebUI & Scan Tent) - What to do with it (Keyword Spotting and EVT) - Thinking about text recognition - Live demo

4 Research perspectives of READ - Recognition of layout and text structures - Recognition of handwriting (Handwritten Text Recognition) - Text recognition with dictionaries - Writer identification - Best-practices for recognition of large amounts of documents - Digital Humanities in archives and scholarly practices

5 How is text recognized? - Recognition of layout and text structures - Recognition of writing - Implementation of dictionaries - Type/Writer identification Perspectives for institutions - Best-practices for recognition of large amounts of documents Perspectives for scholars - How to use in Digital Humanities/DHist: What quality for what purpose

6 READ Partner - University of Innsbruck (co-ordinator / Austria) --> Transkribus - Universitat Politecnica de Valencia (Spain) --> HTR - University College London (United Kingdom) --> Dissemination, e-learning - National Center for Scientific Research Demokritos (Greece) --> Layout Analysis - Democritus University of Thrace (Greece) --> Layout Analysis - University of London Computer Centre (United Kingdom) --> Webinterface - Vienna University of Technology (Austria) --> Layout Analysis, Writer Identification, STent - University of Rostock (Germany) --> HTR, Layout Analysis - Leipzig University (Germany) --> Dictionaries, Named Entities - Xerox Research Centre Europe (France) --> Document Understanding - Ecole Polytechnique Federale de Lausanne (Switzerland) --> Large Scale Demonstrator - National Archives Finland (Finland) --> Large Scale Demonstrator - Passau Diocesan Archives (Germany) --> Large Scale Demonstrator

7 Automated Text Recognition? Direktion der Justiz

8 Automated Text Recognition - Machine learning using neural networks - Processes writing by line, rather than by character - Needs to be trained by being shown document images and transcripts - More training data --> more accurate recognition - Create a model to transcribe and search a collection of documents

9 Recognising printed text - Neural networks can also process printed text with less training data! - Transcribe documents or use OCR engine in Transkribus - Use these transcripts to train a model - Results with 1-2% CER are possible

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14 Bentham model Based on Jeremy Bentham s papers (c English) Written by Bentham and his secretaries Trained on 896 pages using transcripts submitted by volunteers 5-10% CER is possible

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16 Text Recognition: What to expect (Character Error Rate) 1 Writer, 150 pages of material for training: 10% 1 Writer, 450 pages of material for training: 4,4% Same writer, 10 years later, without material for training: 9,2% 1 Writer, 1132 pages of material for training: 3%

17 Text to Image

18 Text Recognition: What to expect (Character Error Rate) X Writers, 800 pages of material for training: 18% X Writers, 100'000+ pages of material for training:?? %

19 20% Character Error Rate Direktion der Justiz

20 13% Character Error Rate Direktion der Justiz

21 Bringing Stakeholders together Direktion der Justiz

22 Bringing Europe together Direktion der Justiz Projects with a Memorandum of Understanding

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24 Web Interfaces - Transcription - Crowdsourcing - Correction - Search/extract - E-Learning (available)

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27 Direktion der Justiz

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29 Direktion der Justiz Bild durch Klicken auf Symbol hinzufügen

30 Keyword Spotting By UPVLC & HIMANIS (IRHT) (search for example for: «scriptor», play-around with confidence ) For further infos:

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32 Transkribus and EVT Edition Visualization Technology Transkribus export to be presented in EVT (by HumaReC)

33 Thinking about text recognition Problems: - Layout analysis - Variety in scripts remain a problem (robustness of models) - Irregularities in abbreviations and expansions Truism: - Recognition according to training Text: - What text?

34 Transkribus: transkribus.eu / @transkribus.eu / transkribus.eu/wiki/ READ: read.transkribus.eu (News) Staatsarchiv Zürich: tobias.hodel@ji.zh.ch Danke!

35 Find out more: 10 Steps Guide 10 Steps Video Transkribus Wiki Direktion der Justiz First Transkribus user day will take place in Vienna, November 2 nd and 3 rd 2017!