OCR

Exploring Automatic Recognition of Labanotation Dance Scores

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[This post describes the research of Michelle de Böck and is based on her MSc Information Sciences thesis.]

Digitization of cultural heritage content allows for the digital archiving, analysis and other processing of that content. The practice of scanning and transcribing books, newspapers and images, 3d-scanning artworks or digitizing music has opened up this heritage for example for digital humanities research or even for creative computing. However, with respect to the performing arts, including theater and more specifically dance, digitization is a serious research challenge. Several dance notation schemes exist, with the most established one being Labanotation, developed in 1920 by Rudolf von Laban. Labanotation uses a vertical staff notation to record human movement in time with various symbols for limbs, head movement, types and directions of movements.

Generated variations of movements used for training the recognizers

Where for musical scores, good translations to digital formats exist (e.g. MIDI), for Lanabotation, these are lacking. While there are structured formats (LabanXML, MovementXML), the majority of content still only exists either in non-digitized form (on paper) or in scanned images. The research challenge of Michelle de Böck’s thesis therefore was to identify design features for a system capable of recognizing Labanotation from scanned images.

Examples of Labanotation files used in the evaluation of the system.

Michelle designed such a system and implemented this in MATLAB, focusing on a few movement symbols. Several approaches were developed and compared, including approaches using pre-trained neural networks for image recognition (AlexNet). This approach outperformed others, resulting in a classification accuracy of 78.4%. While we are still far from developing a full-fledged OCR system for Labanotation, this exploration has provided valuable insights into the feasibility and requirements of such a tool.

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Architectural Digital Humanities student projects

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In the context of our ArchiMediaL project on Digital Architectural History, a number of student projects explored opportunities and challenges around enriching the colonialarchitecture.eu dataset. This dataset lists buildings and sites in countries outside of Europe that at the time were ruled by Europeans (1850-1970).

Patrick Brouwer wrote his IMM bachelor thesis “Crowdsourcing architectural knowledge: Experts versus non-experts” about the differences in annotation styles between architecture historical experts  and non-expert crowd annotators. The data suggests that although crowdsourcing is a viable option for annotating this type of content. Also, expert annotations were of a higher quality than those of non-experts. The image below shows a screenshot of the user study survey.

Rouel de Romas also looked at crowdsourcing , but focused more on the user interaction and the interface involved in crowdsourcing. In his thesis “Enriching the metadata of European colonial maps with crowdsourcing”  he -like Patrick- used the Accurator platform, developed by Chris Dijkshoorn. A screenshot is seen below.  The results corroborate the previous study that the in most cases the annotations provided by the participants do meet the requirements provided by the architectural historian; thus, crowdsourcing is an effective method to enrich the metadata of European colonial maps.

Finally, Gossa Lo looked at automatic enrichment using OCR techniques on textual documents for her Mini-Master projcet. She created a specific pipeline for this, which can be seen in the image below. Her code and paper are available on this Github page:https://github.com/biktorrr/aml_colonialnlp

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