1. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Script-Analysis Tools for the Cultural Heritage – SCRATCH Project review

2. Nederlandse Organisatie voor Wetenschappelijk Onderzoek From Scratch Lambert Schomaker Henny van Schie Tijn van der Zant Fons LaanSveta Zinger

3. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  PhD student: Tijn van der Zant  Postdoc: Sveta Zinger  Scientific programmer: Fons Laan  Coordinator: Lambert Schomaker  Nationaal Archief: Henny van Schie  several ‘invisible’ transcription volunteers on WWW People

4. Nederlandse Organisatie voor Wetenschappelijk Onderzoek   develop Script-retrieval methods strive for Script-Google don’t promise automatic transcription (but develop traditional recognizers ‘under the hood’) Goal

5.  Experimentation platform: KdK archive

6. Nederlandse Organisatie voor Wetenschappelijk Onderzoek SCRipt Analysis Tools for the Cultural Heritage  Traditional OCR: know all character shapes in advance, know the language, know the page layout, then start on page 1, towards the end  Scratch philosophy: coarse to fine-grained access  in an interactive cycle between users and machine  pages annotate  retrieval  paragraphs annotate  retrieval  lines annotate  retrieval  words annotate  retrieval  recognition  characters annotate  retrieval  recognition

7. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Retrieval vs Recognition  Retrieval:  Return(Set_of Image_objects | a_keyword)  Recognition:  Return(Sequence_of Letters | an_image)

8. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  User interface and data-base issues  Fons Laan  Pattern-recognition methods  Lambert Schomaker  Behavior-based Machine Learning, scalability issues  Tijn van der Zant  Language modeling  Sveta Zinger  Transcription/annotation  Henny van Schie and several volunteers Research and activity Tracks

9. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  developments:  line-to-line matching (finished, published)  word-to-line matching (finished, published)  word spotting (continuing, published)  word recognition (continuing, published)  character recognition (continuing, unpublished yet)  layout analysis (published)  parallel computing (continuing, published)  continuous transcription/annotation (continuing process)  GUI, XML and database (continuing)  other types of collections:  Cliwoc / Admiraliteit 1777  Ubbo Emmius @ Elzevirium  migration to public server and Target project Status of project

11. Crude automatic word-zone segmentation Manual word-zone segmentation Unsheared line strips (B/W) t Unsheared Word Zones (B/W) Word labeling t B: Word retrieval and recognition route Word Classifiers tttt Character Classifiers tttt C: Word and character recognition

12. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Line-transcription web site

13. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Example of hit list for a trained word spotter: find the machine errors for abbreviation “RvSt”!

14. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  ~1200 scanned pages / 29k lines  of which 30k lines have now been transcribed via our web interface  Then, of a set of 115k Word Zones (white-space delimited text chunks), 74k words have been labeled by man+machine in concerted action during the last six year: Incremental Machine learning Data

15. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  In order to demonstrate genericity of the approach:  new KdK books will be processed  ‘cliwoc’ admirality books, e.g., captain’s log of 1777, is being processed at the moment  Difficult machine-printed books are a new target, e.g., Ubbo Emmius (1616). Rerum Frisicarum Historia, published by Elzevirium. Data

16. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  Several non-KdK collections (captain’s logs) have been ‘ingested’ by the system (Monk@ai). Page and line segmentation methods could be used without much problems  A ‘JAVA’ tool was developed for manual segmentation and labeling of words (in addition to the automatic method). This tool allows for fine-tuned word and letter harvesting.  An independent tool was developed for high-quality refinement of line transcriptions. This tool will now be migrated to WWW  New books (~37 KdK, ~1000 pages each) will be scanned Data, developments

17. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Patch9x9 Kohonen map 33x33 cells trained on 1.6M patches

18. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Examples of Patch9x9Quadrants feature Amsterdam Groningen 4x33x33

19. Nederlandse Organisatie voor Wetenschappelijk Onderzoek “typical results” (difficult samples, correct) aanhangig Officier overweging zijn zulks

20.  for nearest-neighbour matching Lambert Schomaker – ICCS Computational Humanities 1/6/2010

22. Advances in writer identification and verification – Lambert Schomaker

23. Lambert Schomaker – ICCS Computational Humanities 1/6/2010  #Labels as a function of ‘days on-line’, KdK 1903

24. Lambert Schomaker – Digihist 1/7/2010  #Labels, total as a function of ‘days on-line’, KdK 1903,1893,1897

25. start t=0 synchronized Lambert Schomaker – Digihist 1/7/2010

26. Can we go from pixel to meaning? ... without going through the language (=ASCII) bottleneck?  Idea: take a semantic category or concept  Collect positive examples of text-line images  Sample random, negative examples  Train a binary classifier (SVM) to detect the presence of lines containing the concept Lambert Schomaker – Digihist 1/7/2010

27. Image-feature based ‘municipality’ detector ConceptN labeled Precision (%) Recall (%) City Names 4837677 Lambert Schomaker – Digihist 1/7/2010

28. Image-feature based ‘proper name’ detector ConceptN labeled Precision (%) Recall (%) City Names 4837677 Proper Names 3347981 Lambert Schomaker – Digihist 1/7/2010

29. Image-feature based ‘personal role’ detector  “ambtenaren”  “vervanger”  “president”  “verpleegde” ConceptN labeled Precision (%) Recall (%) City Names 4837677 Proper Names 3347981 Personal Roles 16465 Lambert Schomaker – Digihist 1/7/2010

30.  X-position and semantics, Bayesian modeling Lambert Schomaker – Digihist 1/7/2010

31. From pixel to meaning  these are examples of ‘data mining’  regular handwriting recognition (transcoding into ASCII) is not always needed to get at meaning  with sufficient examples, abstract concepts can be learned  interaction and iteration  e-Science Lambert Schomaker – Digihist 1/7/2010

32. Nederlandse Organisatie voor Wetenschappelijk Onderzoek

33. Example entry wind & weather ‘gps’ coordinates geographical name event

34. Nederlandse Organisatie voor Wetenschappelijk Onderzoek

36.  Important paper in IEEE PAMI (vd Zant et al): biologically inspired features for word recognition (89%)  Dissertation: end 2009 (TvdZ)  Preliminary word-recognition results for other collections:  Admiraliteit, 1777: 93% KohSOM of patches, 4-quadrants  Ubbo Emmius, Latin 95% (idem)  KdK-51+ 92% (idem, +MLP)  KdK-51+ (vd Zant) 93+% Engineered version of IEEE PAMI  New books will be scanned  Ubbo Emmius: scanning was improvised, digital camera Obtained 15kEuro from Gratama Foundation for serious scanning (Arnold Meijster) Results

37. Nederlandse Organisatie voor Wetenschappelijk Onderzoek Future  Target project: 32 MEuro, 15 MEuro funded, 500kEuro for us.  massive, long-term data storage and access methods, from bioinformatics, astronomy to cultural heritage  about 6 person years  New collections are being ‘ingested’: Qumran Scrolls, new institutions are contacting us  we are aiming at a second ‘big’ journal article, to be submitted end 2009

38. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  1 [J] van der Zant, T., Schomaker, L.R.B. & Haak, K. (2008). Handwritten-word spotting using biologically inspired features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol x(x), pp. xxx-xxx. [accepted]  2 [J] Schomaker, L.R.B., Franke, K., Bulacu, M. (2007). Using codebooks of fragmented connected- component contours in forensic and historic writer identification. In: Pattern Recognition Letters, 28(6), p. 719-727.  3 [C] Schomaker, L.R.B. (2008). Word mining in a sparsely-labeled handwritten collection, Document Recognition and Retrieval XV, IS&T/SPIE International Symposium on Electronic Imaging, pp. xxx-xxx.  4 [C] van der Zant, T., Schomaker, L.R.B., Valentijn, E. (2008). Large-scale parallel document-image processing, Document Recognition and Retrieval XV, IS&T/SPIE International Symposium on Electronic Imaging, pp. xxx-xxx.  5 [C] Schomaker, L.R.B. (2007). Retrieval of handwritten lines in historical documents. In: Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR 2007), IEEE Computer Society, pp. xxx-xxx, vol. x, 23 - 26 September, Curitiba, Brazil.  6 [C] Bulacu, M., van Koert, R., Schomaker, L.R.B., van der Zant, T. (2007). Layout analysis of handwritten historical documents for searching the archive of the Cabinet of the Dutch Queen. In: Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR 2007), IEEE Computer  Society, pp. xxx-xxx, vol. x, 23 - 26 September, Curitiba, Brazil. Publications [j]=journal, [c]=peer-reviewed proceedings

39. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  7 [C] Bulacu, M., Schomaker, L.R.B. (2007) Automatic handwriting identification on medieval documents. In: Proceedings of 14th International Conference on Image Analysis and Processing (ICIAP 2007), IEEE Computer Society, 11 - 13 September, Modena, Italy, pp. 279-284.  8 [CB] Schomaker, L.R.B. (2007). Reading Systems: An introduction to Digital Document Processing. In: B.B. Chaudhuri (Ed.), Digital Document Processing, Springer-Verlag: Guildford, Surrey, UK. ISBN: 18462-8501-1, p. 1-28.  9 [C] Bulacu, M. & Schomaker, L.R.B. (2007). Automatic handwriting identification on medieval documents, Proc. of 14th Int. Conf. on Image Analysis and Processing (ICIAP 2007), IEEE Computer Society, pp. 279- 284, 11 - 13 September, Modena, Italy.  10 [C] van der Zant, T., Schomaker, L.R.B., Wiering, M. & Brink, A. (2006). Cognitive Developmental Pattern Recognition: Learning to Learn. IEEE SMC 2006, p. 1208-1213.  11 [C] S. Zinger, J. Nerbonne, L.R.B. Schomaker, H. van Schie, "Script Analysis Tools for the Cultural Heritage: text line matching for historical handwritten document retrieval", SIREN 2007, Scientific Information and communication technology Research Event Netherlands, p. 49, Delft (the Netherlands), October 2007. Publications

40. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  12 [C] S. Zinger, J. Nerbonne, L. Schomaker, H. van Schie, "Content-based text line comparison for historical document retrieval", Computational Phonology workshop, Recent Advances in Natural Language Processing conference, RANLP-2007, pp. 79-84, Borovets (Bulgaria), September 2007.  13 [C] S. Zinger, J. Nerbonne, L.R.B. Schomaker, H. van Schie, "Script Analysis Tools for the Cultural Heritage: statistics on queries and line matching", SIREN 2006, Scientific Information and communication technology Research Event Netherlands, p. 91, Utrecht (the Netherlands), October 2006.  14 [P] Schomaker, L.R.B., Zant, T. van der & Bogaarts, J. (2005). NWO/Catch project "Scratch" - Script-Analysis Tools for the Cultural Heritage. A Pilot Experiment on sparse- knowledge search methods for handwritten collections. Informatica Platform Nederland (IPN), Siren, October 6th, Eindhoven, The Netherlands.  15 [P] M. Bulacu, A. Brink, T. van der Zant, L. Schomaker (2009) Recognition of handwritten numerical fields in a large single-writer historical collection. In: Proceedings of the 10th Int. Conf. on Document Analysis and Recognition (ICDAR 2009), 26 - 29 July, Barcelona, Spain Publications

41. Nederlandse Organisatie voor Wetenschappelijk Onderzoek  Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.  Schomaker, L.R.B., Franke, K. & Bulacu, M. (2007). Using codebooks of fragmented connected-component contours in forensic and historic writer identification, Pattern Recognition Letters, 28(6), p. 719-727.  L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.  M. Aussems, A. Brink (2009) Digital palaeography In: M. Rehbein, P. Sahle, and T. Schassan (Eds.), Kodikologie und Paläographie im Digitalen Zeitalter / Codicology and Palaeography in the Digital Age, ser. Schriftenreihe des Instituts für Dokumentologie und Editorik, Norderstedt: Books on Demand GmbH, 2009, vol. 2. ISBN: 978-3-8370-9842-6  Article [forthcoming]. Axel Brink, Jinna Smit, Marius Bulacu & Lambert Schomaker, "Quill Dynamics Feature for writer identification in historical documents". Publications – writer identification