Optical Recognition of Musical Notation

Op= tical Recognition of Musical Notation

Mus 253/CS 275

Stanford University

Optical Music Recognition (OMR)

Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

2

Optically recognized input

nAdvanta= ges

qInteres= ting intellectual challenge

qCan out= put MIDI files

nDisadva= ntages

qNo cont= rol over quality of input materials

qMust be tailored to particular code for notation software (inherits its weaknesses)

qNo proo= f of efficiency

Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

3

Problems of image quality

n<= /span>Ideally

= qStaff lines are straight

qSpacing is uniform

qThe scanned material is clean (unspotted)

qSlurs are symmetrical

qBeams are parallel

qAll lines are unbroken

n<= /span>Reality is different!

Examp= les from Droettboom and Fujinaga

Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

4

Problems of graphical context

=

nSome s= ymbols affect the interpretation of pitch

= nKey signatures

= nOctave alterations Û Ã

nSome s= ymbols affect the interpretation of duration

= nMeter signatures

= nTempo indicators

= nFermatas U=

nSome s= ymbols affect dynamics and technique

= nDynamics marks ¹ § Ä

= nRepetition of note-groups Ô, of sections Þ %

= nInstrumental technique ¡ v= ³

n

Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

5

Problems of object-configuration

Examp= les from Droettboom and Fujinaga

Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

6

Problems of superimposition

Captured: notes, rests

Missed: slurs, pedal marks

Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

7

Problems of performance evaluation

<= span style=3D'font-size:87%'>nHow to evaluate musical accuracy?

<= span style=3D'font-size:87%'>nHow to musical quality?

nHow to compare different kinds of musical output <= /span>(MIDI, written scores, etc.)?

<= span style=3D'font-size:87%'>nHow to weigh speed against these?

=

<= span style=3D'visibility:hidden'>n

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Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

8

Performance evaluation: Examples

= nComputing in Musicology (the yearbook series of CCARH) did a controlled test of OMR performance in 1993.

= nAs the emphasis switched to producing MIDI files from “recognized” scores (late 90s), performance necessarily improved (MIDI only accommodates five parameters of music= ).

= nMost OMR programs perform better from scores which have been type= set by computer.

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Mus 253/OMR

Elean= or Selfridge-Field (1996-2005)

9

S= harpEye: Subjective performance = evaluation (2005)

n<= /span>Strengths:

qExports to MusicXML (facilitating import to Finale and MuseData)

qDeveloper responsive to requests for improvements

qHandles complex textures [e.g., Beethoven piano sonatas] decently

q

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Optical recognition of musical notation

Since data entry for musical notation is laborious, great hope has been pi= nned on optical recognition of music. Over the first fifteen years of software = development (c. 1985-2000), the results were modest. Some improvements have been noted in recent years.

There are several problems inherent in optical recognition. These are disc= ussed in subsequent slides. Basica= lly, the developer of OMR software is a middleman poised between working materi= als (the scores to be recognized) and the output software, which will have its= own requirements for formatting and sequence of information.

Problems of image quality

One basic problem of OMR is that the quality and consistency of printed sc= ores are both lower than imagined. The human process of reading is greatly assi= sted by a universal tendency to imagine regularity and patterning to exist in materials that are varying in quality.

Some representative examples in this panel are:

Three-note example: beams are not parallel; noteheads are not placed in co= nsistent relationship to stems. Treble-clef example: staff lines are slightly irregular; “G” (a space note) is overlapping the F line. S= lur example: tapered ends (generally considered correct) are not as thick as c= entral areas; configurations are not Chord example: stem is discontinuous. Note placement relative to stem is inconsistent; staccato dot is off center.= 3;

Problems of graphical context

Some of the problems of optical recognition occur because certain symbols = in notated scores are relational. Clef signs and key signatures offer two good exampl= es. A natural sign (cancelled acciden= tal) in the midst of a score can easily be recognized by software, but if the k= ey signature was misinterpreted at the start the results may still be peculiar.

Problems of object configuration

One problem that music recognition shares with text recognition is that of= idiosyncracies of object configuration. Just as the letter A can be drawn in a lot of different ways (a, a, a, a, a, a, a), so too musical objects can be drawn diffe= rently. In these examples, the sharp signs, beam inclinations, and stem lengths we= re isolated and classified.

Problems of superimposition

Almost all efforts to recognize music optically begin with attempts to iso= late individual symbols. The order in which objects are isolated may affect the= success rate of recognition because of the frequency with which symbols partially overlay one another. Notes overlay staves (that is, they are uninterpretab= le without them).

A scan of the Chopin Waltz shown here captured notes and rests without dif= ficulty but missed most slurs and pedal marks.

Problems of performance evaluation&#= 13;

One of the biggest problems of OMR is determ= ining how to fairly evaluate results. One can count the objects recognized, but there is no common agreement as to what an “object” is. Should a not= ehead (open or filled), a stem, and a flag each count one point? Or are they all= part of an indivisible “note”?

Overall, systems outputting MIDI data give t= he impression of greater accuracy than those replicating the original score. The main reason for this is specious: MIDI data requir= es many fewer parameters than full-score notation.

How much does efficiency count? How can= it be measured?

To approach this question, we had the same H= aydn symphony input (a) via MuseData and (b) via optical recognition and post-editing in SCORE. This experiment was run in 1994. (The results would undoubtedly = be different today). In the end, both efforts took approximately 10 hours. In the case of MuseData, 9:20 was spent in = data acquisition (two phases) and 20 minut= es in clean-up. In the other case, 20 minutes was spent in data acquisition and 9 hours in post-editing (many barlines = were incorrectly placed; some accidentals were wrong, etc.).

Performance evaluation: Examples

1.Computing in Musicology (1993-94) ran a systematic comparison of efforts then current. Only 6 of the 37 groups canvassed returned examples showing their successes in scanning two set pieces—(1) a four-part violin part and= (2) a two-bar grade-2 piano piece. At the time, large objects, such as clef si= gn, we re difficult to identify.

2.

2.MIDI files produce= d by OMR have the same downside as others: the graphical details filtered out n= eed to be added by hand to produce scores of quality comparable to the original music.

3.

3.Scores generated f= rom scans of computer-typeset music confront a legal paradox: such scores are almost invariably under copyright and cannot be distributed from unauthori= zed copies.

4.

SharpEye: Subje= ctive Evaluation

We have run numerous experiments= with SharpEye over the past year and have found it useful in various ways. SharpE= ye exports data to MusicXML rather than to the code of a specific notation program or= to MIDI. This gives a certain versatility to the data captured.

Inevitably we have found several= bugs in the program, but we have found that developer to be quite responsive in providing remedies.

The most impressive measure of <= i>SharpEye’s success has been in being able to capture Beethoven piano sonatas with reasonable accuracy. The complex musical texture guarantees dense graphical information, but SharpEye (with some hand editing) seems to handle = this complexity relatively well.

Some other music we have used Sh= arpEye to recognize include a complete Handel opera (from a nineteenth-cenury pri= nt) and German folksongs with piano accompaniments (also from early prints).&#= 13;

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FULLSCR_HREF = : FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref; if(PPTNav.event.ctrlKey) { var w =3D (window.screen.availWidth * 1.0) / 2.0 var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top= =3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h ); if( PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } else { win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" ); if( PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } } function ToggleVNarration() { rObj=3DPPTSld.document.all("NSPlay") if( rObj && !PPTSld.g_animUseRuntime ) { if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) ) rObj.Play() else if( rObj.playState =3D=3D 2 ) rObj.Pause() else return; } else if( PPTSld.g_animUseRuntime ) { narObj =3D PPTSld.document.all("narrationID") if( narObj ) narObj.togglePause() } } function GetCurSldNum() { obj=3DGetHrefObj(gCurSld) if( obj.mOrigVis =3D=3D 1 ) return obj.mSldIdx else return gCurSld } function GetNumSlds() { if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 ) return GetSldList().mNumVisSlds; else return GetSldList().mList.length } function GetSldNum( href ) { for(ii=3D0; ii 1 ) PopSldList(); else if( !IsFramesMode() ) { if( gLoopCont ) GoToFirst() else EndShow() } } function GoToPrevSld() { ii=3DgCurSld-1 if( ii > 0 ) { obj=3DGetHrefObj(ii) while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) ) obj=3DGetHrefObj(--ii) if( ii =3D=3D 0 ) ii=3D1 GoToSldNum(ii) } } function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) } function GoToLast() { ii=3DGetSldList().mList.length if( ii !=3D gCurSld ) GoToSld( GetHrefObj(ii).mSldHref ) } function GoToSldNum( num ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue obj =3D GetHrefObj( num ) obj.mVis=3D1 gPrevSld=3DgCurSld gCurSld =3D num; PPTSld.location.replace(MHTMLPrefix+obj.mSldHref) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function GoToSld( href ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue GetHrefObj( GetSldNum(href) ).mVis=3D1 PPTSld.location.replace(MHTMLPrefix+href) } function SldUpdated( id ) { if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return gPrevSld=3DgCurSld gCurSld=3DGetSldNum(id) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO= bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) } function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) } function CloseWindow() { if( HideMenu() ) return; var event =3D PPTSld.event; if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D= =3D32 || event.type=3D=3D"click" ) ) window.close( self ); CatchNumKeys( self, event ); } function Unload() { gIsEndShow=3D0; } function SetupEndShow() { gIsEndShow=3D1; PPTSld.document.body.scroll=3D"no"; PPTSld.document.onkeypress=3DCloseWindow; PPTSld.document.onclick=3DCloseWindow; PPTSld.document.oncontextmenu=3D_CM; } function EndShow() { if( IsFramesMode() ) return if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue doc=3DPPTSld.document doc.open() doc.writeln('

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') doc.close() } function SetSldVisited(){ GetSldList().mList[gCurSld-1].mVisited=3Dtrue } function IsSldVisited(){ return GetSldList().mList[gCurSld-1].mVisited } function hrefList( sldHref, visible, advDelay, advClk ) { this.mSldHref=3D this.mNtsHref =3D sldHref this.mOrigVis=3D this.mVis =3D visible this.mVisited=3D false this.mAdvDelay=3D advDelay this.mAdvOnClk=3D advClk } function SldList(arr,curSld,fEnd) { this.mCurSld =3D curSld; this.mList =3D new Array(); var idx =3D 1; for(ii=3D0;ii 0) { PushSldList(sldList,fEnd); gCurSld =3D 1; } else if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue } function PushSldList(arr,fEnd) { var ii =3D gSldStack.length; gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd); GoToSld( gSldStack[ii].mList[0].mSldHref ); } function PopSldList() { if (gSldStack[gSldStack.length-1].fEndShow) EndShow() else { gCurSld =3D gSldStack[gSldStack.length-1].mCurSld; gSldStack[gSldStack.length-1] =3D null; gSldStack.length--; var sldList =3D gSldStack[gSldStack.length-1]; GoToSld( sldList.mList[gCurSld - 1].mSldHref ); } } var custShowList=3Dnew Array(); function ImgBtn( oId,bId,w,action ) { var t=3Dthis t.Perform =3D _IBP t.SetActive =3D _IBSetA t.SetInactive=3D _IBSetI t.SetPressed =3D _IBSetP t.SetDisabled=3D _IBSetD t.Enabled =3D _IBSetE t.ChangeIcon =3D null t.UserAction =3D action t.ChgState =3D _IBUI t.mObjId =3D oId t.mBorderId=3D bId t.mWidth =3D w t.mIsOn =3D t.mCurState =3D 0 } function _IBSetA() { if( this.mIsOn ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _IBSetI() { if( this.mIsOn ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0 } } function _IBSetP() { if( this.mIsOn ) { obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 ) obj.style.posLeft+=3D1; obj.style.posTop+=3D1 } } function _IBSetD() { obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 ) obj.style.posTop=3D0 } function _IBSetE( state ) { var t=3Dthis GetObj( t.mBorderId ).style.visibility=3D"visible" if( state !=3D t.mIsOn ) { t.mIsOn=3Dstate if( state ) t.SetInactive() else t.SetDisabled() } } function _IBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( t.ChangeIcon ) { obj=3DGetObj(t.mObjId) if( t.ChangeIcon() ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth else obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth } t.SetActive() } } function _IBUI( clr1,clr2,nextState ) { var t=3Dthis SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) obj=3DGetObj( t.mObjId ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj= .style.posTop t.mCurState=3DnextState return obj } function TxtBtn( oId,oeId,action,chkState ) { var t=3Dthis t.Perform =3D _TBP t.SetActive =3D _TBSetA t.SetInactive=3D _TBSetI t.SetPressed =3D _TBSetP t.SetDisabled=3D _TBSetD t.SetEnabled =3D _TBSetE t.GetState =3D chkState t.UserAction =3D action t.ChgState =3D _TBUI t.mObjId =3D oId t.m_elementsId=3D oeId t.mIsOn =3D 1 } function _TBSetA() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gHiliteClr,gShadowClr,0,0 ) } function _TBSetI() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) } function _TBSetP() { if( this.mIsOn ) this.ChgState( gShadowClr,gHiliteClr,1,1 ) } function _TBSetD() { this.ChgState( gFaceClr,gFaceClr,0,0 ) this.mIsOn =3D 0 } function _TBSetE() { var t=3Dthis if( !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) else t.ChgState( gShadowClr,gHiliteClr,1,1 ) t.mIsOn =3D 1 } function _TBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( !t.GetState ) return if( t.GetState() ) t.SetPressed() else t.SetActive() } } function _TBUI( clr1,clr2,lOffset,tOffset ) { SetBorder( GetObj( this.mObjId ),clr1,clr2 ) Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) } function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle= ft } function SetBorder( obj, upperLeft, lowerRight ) { s=3Dobj.style; s.borderStyle =3D "solid" s.borderWidth =3D 1 s.borderLeftColor =3D s.borderTopColor =3D upperLeft s.borderBottomColor=3D s.borderRightColor =3D lowerRight } function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() } function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() } function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() } function BtnOnUp() { b=3DGetBtnObj() if( b !=3D null ) b.Perform() else Upd() } function GetNtsState(){ return parent.gNtsOpen } function GetOtlState(){ return parent.gOtlOpen } function GetOtlTxtState(){ return parent.gOtlTxtExp } function NtsBtnSetFlag( fVal ) { s=3Ddocument.all.item( this.m_flagId ).style s.display=3D"none" if( fVal ) s.display=3D"" else s.display=3D"none" } function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etActive() } function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etInactive() } var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"= THREEDFACE" var gBtnArr =3D new Array() gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan= e,GetOtlState ) gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt= lPane,GetOtlState ) gBtnArr["nb_nts"] =3D new TxtBtn( "nb_nts","nb_ntsElem",parent.ToggleNtsPan= e,GetNtsState ) gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","nb_prevBorder",30,parent.GoToP= revSld ) gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","nb_nextBorder",30,parent.GoToN= extSld ) gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent= .FullScreen ) gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa= rent.FullScreen,null ) gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border; gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent= .FullScreen,null ) gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border; 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1	Optical Recognit= ion of Musical Notation Mus 253/CS 275 Stanford University Optical Music Recognition (OMR)
2	Optically recogn= ized input Advantages Interesting intellectual challenge Can output MIDI files Disadvantages No control over quality of input materials Must be tailored to particular code for notation software (inherits= its weaknesses) No proof of efficiency
3	Problems of image quality Ideally Staff lines are straight Spacing is uniform The scanned material is clean (unspotted) Slurs are symmetrical Beams are parallel All lines are unbroken Reality is different!
4	Problems of grap= hical context Some symbols affect the interpretation of pitch Key signatures Octave alterations Û Ã Some symbols affect the interpretation of duratio= n Meter signatures Tempo indicators Fermatas U Some symbols affect dynamics and technique= Dynamics marks ¹ § Ä Repetition of note-groups Ô, of sections Þ = % Instrumental technique ¡ v = ³
5	Problems of object-configuration
6	Problems of superimposition
7	Problems of perf= ormance evaluation How to evaluate musical accuracy? How to musical quality= ? How to compare different kinds of musical output (MIDI, written scor= es, etc.)? How to weigh speed against these?
8	Performance eval= uation: Examples Computing in Musicology (the yearbook series of CCARH) did a control= led test of OMR performance in 1993. As the emphasis switched to producing MIDI files from “recognized” scores (late 90s), performance necessarily improved (MIDI only accommodates five parameters of music). Most OMR programs perform better from scores which have been typeset= by computer.
9	SharpEye: Subjec= tive performance evaluation (2005) Strengths: Exports to MusicXML (facilitating import to Finale and MuseData) Developer responsive to requests for improvements Handles complex textures [e.g., Beethoven piano sonatas] decently

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	Problems of i= mage quality

	One basic proble= m of OMR is that the quality and consistency of printed scores are both lower than imagined. The human process of reading is greatly assisted by a universal tendency to imagine regularity and patterning to exist in materials that = are varying in quality.

	Some representat= ive examples in this panel are:

	Three-note examp= le: beams are not parallel; noteheads are not placed in consistent relationship to stems.
	Treble-clef exam= ple: staff lines are slightly irregular; “G” (a space note) is overlappi= ng the F line.
	Slur example: ta= pered ends (generally considered correct) are not as thick as central areas; configurations are not Chord example: stem is discontinuous. Note placeme= nt relative to stem is inconsistent; staccato dot is off center.


	Problems of p= erformance evaluation

	One of the bigge= st problems of OMR is determining how to fairly evaluate results. One can count the objects recogn= ized, but there is no common agreement as to what an “object” is. Should a notehead (open or filled), a stem, and a flag each count one poi= nt? Or are they all part of an indivisible “note”?

	Overall, systems= outputting MIDI data give the impression of greater accuracy than those replicating = the original score. The main reason for this is specious: MIDI data requires = many fewer parameters than full-score notation.

	How much does ef= ficiency count? How can it be measured?

	To approach this= question, we had the same Haydn symphony input (a) via MuseData and (b) via optical recognition and post-editing in SCORE. This experiment was run in 1994. (= The results would undoubtedly be different today). In the end, both efforts t= ook approximately 10 hours. In the case of MuseData, 9:20 was spent in data acquisition (two phases) and 20 minutes in clean-up. In the other case, 20 minutes was spent in data acquisition and 9 hours in post-editing (many barlines were incorrectly placed; some accidentals were wrong, etc.).


	Performance e= valuation: Examples

	1.= Computing in Musicology (1993-94) ran a syst= ematic comparison of efforts then current. Only 6 of the 37 groups canvassed returned examples showing their successes in scanning two set pieces—(1) a four-part violin part and (2) a two-bar grade-2 piano piece. At the time, large objects, such as clef sign, we re difficult to identify.

	2.MIDI files produced by OMR have the same downside as others: the graphical details filtered out need to be added by hand to produce scores= of quality comparable to the original music.

	3.Scores generated from scans of computer-typeset music confront a= legal paradox: such scores are almost invariably under copyright and cannot be distributed from unauthorized copies.


	*SharpEye*: Subjective Evaluation

	We have run nume= rous experiments with SharpEye over the past year and have found it use= ful in various ways. SharpEye exports data to MusicXML rather than to = the code of a specific notation program or to MIDI. This gives a certain versatility to the data captured.

	Inevitably we ha= ve found several bugs in the program, but we have found that developer to be quite responsive in providing remedies.

	The most impress= ive measure of SharpEye’s success has been in being able to capture Beethoven piano sonatas with reasonable accuracy. The complex musical tex= ture guarantees dense graphical information, but SharpEye (with some ha= nd editing) seems to handle this complexity relatively well.

	Some other music= we have used SharpEye to recognize include a complete Handel opera (from a nineteenth-cenury print) and German folksongs with piano accompaniments (= also from early prints).


	Optical recog= nition of musical notation

	Since data entry= for musical notation is laborious, great hope has been pinned on optical recognition = of music. Over the first fifteen years of software development (c. 1985-2000), the results were modest. Some improvements have been noted in recent years.

	There are severa= l problems inherent in optical recognition. These are discussed in subsequent slides. Basically, the deve= loper of OMR software is a middleman poised between working materials (the scor= es to be recognized) and the output software, which will have its own requirements for formatting and sequence of information.


	Problems of g= raphical context

	Some of the prob= lems of optical recognition occur because certain symbols in notated scores are relational. Clef signs and key signatures offer two good examples. A natural sign (cancelled accide= ntal) in the midst of a score can easily be recognized by software, but if the = key signature was misinterpreted at the start the results may still be peculi= ar.


	Problems of o= bject configuration

	One problem that= music recognition shares with text recognition is that of idiosyncracies of obj= ect configuration. Just as the letter A can be drawn in a lot of different wa= ys (a, a, a, a, a, a, a), so too musical objects can be drawn differently. In these examples, t= he sharp signs, beam inclinations, and stem lengths were isolated and classified.


	Problems of superimposition

	Almost all effor= ts to recognize music optically begin with attempts to isolate individual symbo= ls. The order in which objects are isolated may affect the success rate of recognition because of the frequency with which symbols partially overlay= one another. Notes overlay staves (that is, they are uninterpretable without them).

	A scan of the Ch= opin Waltz shown here captured notes and rests without difficulty but missed most sl= urs and pedal marks.