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Gamera A Toolkit for Structured Document Recognition including Music

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Presentation on theme: "Gamera A Toolkit for Structured Document Recognition including Music"— Presentation transcript:

1 Gamera A Toolkit for Structured Document Recognition including Music
Ichiro Fujinaga McGill University with Michael Droettboom, Karl MacMillan, G. Sayeed Choudhury, Tim DiLauro, Mark Patton, Teal Anderson Levy Project II Digital Knowledge Center Sheridan Libraries Johns Hopkins University

2 Content Levy Project Optical Music Recognition Gamera
Levy Sheet Music Collection Digital Workflow Management Optical Music Recognition Gamera Guido / NoteAbility

3 Lester S. Levy Collection

4 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)

5 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

6 Optical Music Recognition (OMR)
Trainable open-source OMR system in development since 1984 Staff recognition and removal Lyric removal Stems and notehead removal Music symbol classifier Score reconstruction Lyric classifier?

7 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Commercial OCR systems are often inadequate for non-standard documents The market for specialized recognition of historical documents is very small Researchers performing document recognition often “re-invent” the basic image processing wheel

8 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

9 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

10 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)

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

12 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()); }

13 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

14 Gamera: Interface (screenshot in Linux)

15 Gamera: Interface (screenshot in Linux)

16 Histogram (screenshot in Linux)

17 Thresholding (screenshot in Linux)

18 Thresholding (screenshot in Linux)

19 Staff removal: Lute tablature

20

21 Classifier: Lute (screenshot in Linux)

22 Staff removal: Neums

23 Classifier: Neums (screenshot in Linux)

24 Greek example

25 GUIDO Music Notation Format H. Hoos, K. Renz, J. Kilian
“A formal language for score-level representation” Plain text: readable, platform independent Extensible and flexible Adequate representation NoteServer: Web/Windows GUIDO/XML NoteAbility (K. Hamel)

26 GUIDO: An example { [ \beamsOff | \clef<"treble"> \key<"D"> f#*1/8. g*1/16 | a*1/4. d2*1/8 d*1/4. c#*1/8 | e1*1/2 _*1/4 f#*1/8. g*1/16 | c#2*1/4. b1*1/8 a*1/4. g*1/8 | | e#*1/2 f#*1/4 f#*1/8. g*1/16 | e1*1/2 _*1/4 f#*1/8 g | c#2*1/4. b1*1/8 a*1/4. c#*1/8 ],

27 NoteAbility Demo

28 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 Beta version available on Linux OS X version in preparation

29 Acknowledgements National Science Foundation
Institute of Museum and Library Services The Levy Family

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

31 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


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