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Applica'onexa
PhotoOCR
Problemdescri
andpipeline
MachineLearning
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ThePhotoOCRproblem
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PhotoOCRpipeline
1.Textdetec'on
2.Charactersegmenta'on
3.Characterclassifica'on
NA
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Image Textdetec8onCharacter
segmenta8on
Ch
reco
PhotoOCRpipeline
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Applica'onexa
PhotoOCR
Slidingwindo
MachineLearning
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Textdetec8on Pedestriande
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Supervisedlearningforpedestriandetec8on
pixelsin82x36imagepatches
Nega'veexamples
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Slidingwindowdetec8on
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Slidingwindowdetec8on
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Slidingwindowdetec8on
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Slidingwindowdetec8on
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Textdetec8on
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Textdetec8on
Posi'veexamples Nega'veexample
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Textdetec8on
[DavidWu]
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1DSlidingwindowforcharactersegmenta8on
Posi'veexamples Nega'veexample
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PhotoOCRpipeline
1.Textdetec'on
2.Charactersegmenta'on
3.Characterclassifica'on
NA
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Applica'onexa
PhotoOCR
GeInglotso
data:Ar'ficiadatasynthes
MachineLearning
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Characterrecogni8on
N
I
A
Q
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Ar8ficialdatasynthesisforphotoOCR
Realdata
Abcdefg
Abcdefg
Abcdef
AbcdefgAbcdefg
[AdamCoatesandTaoWang]
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Synthesizingdatabyintroducingdistor8ons
[AdamCoatesandTaoWang]
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Synthesizingdatabyintroducingdistor8ons:Speechr
Originalaudio:
Audioonbadcellphoneconnec'on
Noisybackground:Crowd
Noisybackground:Machinery
[www.pdsounds.org]
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Synthesizingdatabyintroducingdistor8ons
Distor'onintroducedshouldberepresenta'onofthet
noise/distor'onsinthetestset.
Audio:
Backgroundnoise,
badcellphoneconn
Usuallydoesnothelptoaddpurelyrandom/meaningle
toyourdata.
intensity(brightness)
randomnoise
[AdamCoatesandTaoWang]
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DiscussionongeJngmoredata
1.
Makesureyouhavealowbiasclassifierbeforeexpeffort.(Plotlearningcurves).E.g.keepincreasingth
offeatures/numberofhiddenunitsinneuralnetwoyouhavealowbiasclassifier.
2. Howmuchworkwoulditbetoget10xasmuchdacurrentlyhave?
-Ar'ficialdatasynthesis- Collect/labelityourself
- Crowdsource(E.g.AmazonMechanicalTurk)
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DiscussionongeJngmoredata
1.
Makesureyouhavealowbiasclassifierbeforeexpeffort.(Plotlearningcurves).E.g.keepincreasingth
offeatures/numberofhiddenunitsinneuralnetwoyouhavealowbiasclassifier.
2. Howmuchworkwoulditbetoget10xasmuchdacurrentlyhave?
-Ar'ficialdatasynthesis- Collect/labelityourself
- Crowdsource(E.g.AmazonMechanicalTurk)
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Applica'onexa
PhotoOCR
Ceilinganalysis:
partofthepipeworkonnext
MachineLearning
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Es8ma8ngtheerrorsduetoeachcomponent(ceiling
Image Textdetec8onCharacter
segmenta8on
Ch
reco
Whatpartofthepipelineshouldyouspendthemost'
tryingtoimprove?
Component AccuracOverallsystem 72%
Textdetec'on 89%
Charactersegmenta'on 90%
Characterrecogni'on 100%
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Anotherceilinganalysisexample
Facerecogni'onfromimages
(Ar'ficialexample)
Logistic regresFace detection!
Camera!image!
Eyes segmentation!
Nose segmentation!Mouth
segmentation
Preprocess!(remove background)!
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Component
Overallsystem
Preprocess(remove
background)
Facedetec'on
Eyessegmenta'on
Nosesegmenta'on
Mouthsegmenta'on
Logis'cregression
Anotherceilinganalysisexample
Logistic regressFace detection!
Camera!image!
Eyes segmentation!Nose segmentation!
Mouthsegmentation
Preprocess!(remove background)!
