Problem description and pipeline

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Presentation transcript:

Problem description and pipeline Application example: Photo OCR Problem description and pipeline Machine Learning

The Photo OCR problem LULA B’s ANTIQUE MALL LULA B’s OPEN LULA B’s

2. Character segmentation Photo OCR pipeline 1. Text detection 2. Character segmentation 3. Character classification A N T

Character segmentation Character recognition Photo OCR pipeline Character segmentation Character recognition Image Text detection

Application example: Photo OCR Sliding windows Machine Learning

Text detection Pedestrian detection

Supervised learning for pedestrian detection pixels in 82x36 image patches Positive examples Negative examples

Sliding window detection

Sliding window detection

Sliding window detection

Sliding window detection

Text detection

Text detection Positive examples Negative examples

Text detection Expansion Rectangles around operator [David Wu]

1D Sliding window for character segmentation Positive examples Negative examples Change it to segmentation examples instead:

2. Character segmentation Photo OCR pipeline 1. Text detection 2. Character segmentation 3. Character classification A N T

Getting lots of data: Artificial data synthesis Application example: Photo OCR Getting lots of data: Artificial data synthesis Machine Learning

Character recognition Q A Sort it, spell antique

Abcdefg Artificial data synthesis for photo OCR Real data [Adam Coates and Tao Wang]

Artificial data synthesis for photo OCR Synthetic data Real data [Adam Coates and Tao Wang]

Synthesizing data by introducing distortions [Adam Coates and Tao Wang]

Synthesizing data by introducing distortions: Speech recognition Original audio: Audio on bad cellphone connection Noisy background: Crowd Noisy background: Machinery [www.pdsounds.org]

Synthesizing data by introducing distortions Distortion introduced should be representation of the type of noise/distortions in the test set. Audio: Background noise, bad cellphone connection Usually does not help to add purely random/meaningless noise to your data. intensity (brightness) of pixel random noise 2x2, add noise [Adam Coates and Tao Wang]

Discussion on getting more data Make sure you have a low bias classifier before expending the effort. (Plot learning curves). E.g. keep increasing the number of features/number of hidden units in neural network until you have a low bias classifier. “How much work would it be to get 10x as much data as we currently have?” Artificial data synthesis Collect/label it yourself “Crowd source” (E.g. Amazon Mechanical Turk)

Discussion on getting more data Make sure you have a low bias classifier before expending the effort. (Plot learning curves). E.g. keep increasing the number of features/number of hidden units in neural network until you have a low bias classifier. “How much work would it be to get 10x as much data as we currently have?” Artificial data synthesis Collect/label it yourself “Crowd source” (E.g. Amazon Mechanical Turk)

Ceiling analysis: What part of the pipeline to work on next Application example: Photo OCR Ceiling analysis: What part of the pipeline to work on next Machine Learning

Character segmentation Character recognition Estimating the errors due to each component (ceiling analysis) Character segmentation Character recognition Image Text detection What part of the pipeline should you spend the most time trying to improve? Component Accuracy Overall system 72% Text detection 89% Character segmentation 90% Character recognition 100%

Another ceiling analysis example Face recognition from images (Artificial example) Camera image Preprocess (remove background) Eyes segmentation Face detection Nose segmentation Logistic regression Label Mouth segmentation

Preprocess (remove background) Another ceiling analysis example Camera image Preprocess (remove background) Eyes segmentation Logistic regression Label Nose segmentation Face detection Component Accuracy Overall system 85% Preprocess (remove background) 85.1% Face detection 91% Eyes segmentation 95% Nose segmentation 96% Mouth segmentation 97% Logistic regression 100% Mouth segmentation