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Optical Music Recognition

Optical Music Recognition. Ichiro Fujinaga McGill University 2007. Content. Optical Music Recognition Levy Project Levy Sheet Music Collection Digital Workflow Management Gamera. Optical Music Recognition (OMR). Trainable open-source OMR system in development since 1984

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Optical Music Recognition

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  1. Optical Music Recognition Ichiro Fujinaga McGill University 2007

  2. Content • Optical Music Recognition • Levy Project • Levy Sheet Music Collection • Digital Workflow Management • Gamera

  3. Optical Music Recognition (OMR) • Trainable open-source OMR system in development since 1984 • Staff recognition and removal • Run-length coding • Projections • Lyric removal / classifier • Stems and notehead removal • Music symbol classifier • Score reconstruction Demo

  4. OMR: Classifier • Connected-component analysis • Feature extraction, e.g: • Width, height, aspect ratio • Number of holes • Central moments • k-nearest neighbor classifier • Genetic algorithm

  5. Overall Architecture for OMR Image File Staff removal Segmentation Recognition K-NN Classifier Output Symbol Name Optimization Genetic Algorithm K-nn Classifier Knowledge Base Feature Vectors Best Weight Vector Off-line

  6. Lester S. Levy Collection

  7. Lester S. Levy Collection • North American sheet music (1780–1960) • Digitized 29,000 pieces • including “The Star-Spangle Banner” and “Yankee Doodle” • Database of: • text index records • images of music (8bit gray) • lyrics (first lines of verse and chorus) • color images of cover sheets (32bit)http://levysheetmusic.mse.jhu.edu

  8. Digital Workflow Management • Reduce the manual intervention for large-scale digitization projects • Creation of data repository (text, image, sound) • Optical Music Recognition (OMR) • Gamera • XML-based metadata • composer, lyricist, arranger, performer, artist, engraver, lithographer, dedicatee, and publisher • cross-references for various forms of names, pseudonyms • authoritative versions of names and subject terms • Music and lyric search engines • Analysis toolkit

  9. The problem • Suitable OCR for lyrics not found • Commercial OCR systems are often inadequate for non-standard documents • The market for specialized recognition of historical documents is very small • Researchers performing document recognition often “re-invent” the basic image processing wheel

  10. The solution • Provide easy to use tools to allow domain experts (people with specialized knowledge of a collection) to create custom recognition applications • Generalize OMR for structured documents

  11. Introducing Gamera • Framework for creation of structured document recognition system • Designed for domain experts • Image processing tools (filters, binarizations, etc.) • Document segmentation and analysis • Symbol segmentation and classification • Feature extraction and selection • Classifier selection and combiners • Syntactical and semantic analysis Generalized Algorithms and Methods for Enhancement and Restoration of Archives

  12. Features of Gamera • Portability (Unix, Windows, Mac) • Extensibility (Python and C++ plugins) • Easy-to-use (experts and programmers) • Open source • Graphic User Interface • Interactive / Batchable (scripts)

  13. Scripting Environment (Python) Automatic Plugin Wrapper (Boost) Architecture of Gamera Graphic User Interface (wxWindows) Plugins (Python) Plugins (C++) GAMERA Core (C++)

  14. Example of C++ Plugin // Number of pixels in matrix #include “gamera.hh” #ifdef __area_wrap__ #define NARGS 1 #define ARG1_ONEBIT #endif using namespace Gamera; template <class T> feature_t area(T &m) { return feature_t(m.nrows() * m.ncols()); }

  15. Example of Python Plugin // This filters a list of CC objects import gamera def filter_wide(ccs, max_width): tmp = [] for x in ccs: if x.ncols() > max_width: x.fill_matrix(0) else: tmp.append(x) return tmp

  16. Gamera: Interface(screenshot in Linux)

  17. Gamera: Interface(screenshot in Linux)

  18. Histogram(screenshot in Linux)

  19. Thresholding(screenshot in Linux)

  20. Thresholding(screenshot in Linux)

  21. Staff removal: Lute tablature

  22. Classifier: Lute(screenshot in Linux)

  23. Staff removal: Neums

  24. Classifier: Neums(screenshot in Linux)

  25. Greek example

  26. Conclusions • Gamera allows rapid development of domain-specific document recognition applications • Domain experts can customize and control all aspects of the recognition process • Includes an easy-to-use interactive environment for experimentation • Available on Linux, OS X, and Windows

  27. Projections X-projections Y-projections back

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