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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

Optical Music Recognition

Ichiro Fujinaga

McGill University

2007

content
Content
  • Optical Music Recognition
  • Levy Project
    • Levy Sheet Music Collection
    • Digital Workflow Management
  • Gamera
optical music recognition omr
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

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

lester s levy collection7
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
digital workflow management
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
the problem
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
the solution
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
introducing gamera
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

features of gamera
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)
architecture of gamera

Scripting Environment (Python)

Automatic Plugin Wrapper (Boost)

Architecture of Gamera

Graphic User Interface (wxWindows)

Plugins (Python)

Plugins (C++)

GAMERA Core (C++)

example of c plugin
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());

}

example of python plugin
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

conclusions
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
projections
Projections

X-projections

Y-projections

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