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Deep Learning
From
Script and Address Recognition
to
General Decisions and Solutions
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Marc-Peter Schambach*1971
Dipl. PhysicsPhD Computer Science
Siemens Konstanz, GermanyPostal, Parcel & Airport LogisticsHandwriting Recognition
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Overview
● Postal Automation● Retinal Stimulus Reconstruction● Handwriting Recognition
– HMM
– RNN● Deep Learning
– Geolocation of Images
– Game of Go
– Playing Atari
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I
Postal Automation
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Siemens Postal, Parcel & Airport Logistics GmbH
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II
Neurobiology
Retinal Stimulus Reconstruction
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Retinal Stimulus Reconstruction
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Retinal Stimulus Reconstruction
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Retinal Stimulus Reconstruction
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III
Handwriting Recognition
Hidden Markov Models
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Cursive Script
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Preprocessing
Contour Approximation
Slant Normalization
Base Line Recognition
Skeletization
Feature Generation
Line Approximation
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Character Models
Linear TransformationLinear Transformation
Vector QuantizationVector Quantization
Viterbi AlgorithmViterbi Algorithm
Word List New London
lower
upper
lower
upper
joker
script
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Lexicon-free Recognition
Recurrent HMM
Postprocessing with language models:• N-grams• Lexika• Rules
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Viterbi-Decoding
Word models
Prefix-Trie
• Merge word beginnings
• Maximize probability at word endings
Beam-Search
• Calculate only activated nodes
• Deactivate nodes with low likelihood
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III
Handwriting Recognition
Recurrent Neural Networks
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Recurrent Neural Networks
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Long short term memory
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Vanishing Gradient
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Multi-dimensional Recurrent Neural Networks
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RNN Training
- Backpropagation
- CTC-Layer: Forward-Backward-Alignmentdetermines error function
- Similar to Expectation-Maximization
- Stabilizing by fixed alignment during first training epochs
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Recurrent Neural Networks
Jacobi-Matrix: Shows the input range relevant for classification
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Handwriting recognition with RNN
+ WRR cursive handwriting 95% (lexicon 10k)
+ CRR machine print 99,6%
+ Robust
+ Trains language models
- Slower than HMM (training and recognition)
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III
Reading & InterpretationVision & Understanding
Future Directions
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Vision & Understanding: Operation Pipeline
Image Processing Deep Learning Databases, Rules
Objects of Interest Recognition Interpretation
Deep Learning
Deep Learning Databases, Rules
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IV
Deep Learning
Recent Applications
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Photo Geolocation
with
Convolutional Neural Networks
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Convolutional Neural Networks
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Geolocation Error: PlaNet vs. Humans
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More examples
GeoGuessr Panorama
Ground truth
Human guess
PlaNet guess
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Mastering the Game of Go
with
Deep Neural Networks
and Tree Search
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March 2016: AlphaGo defeats Lee Sedol 4–1
December 2015: AlphaGo defeats Fan Hui 5–0
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Neural Network Architecture
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Neural Network Training Pipeline
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Monte Carlo Tree Search
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Selecting Moves
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AlphaGo: Results
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Playing Atari
with
Deep Reinforcement Learning
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Pong Breakout
SpaceInvaders
Beam RiderSeaquest
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BA C
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Playing Atari: Results
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Round-Up
● Neurobiological background● Script Recognition● Image Classification● Strategy Games● Video Games
What are your problems to be solved?
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Leonardo da Vinci, 1494–1498
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Marc-Peter Schambach*1971
Dipl. PhysicsPhD Computer Science
Siemens Konstanz, GermanyPostal, Parcel & Airport LogisticsHandwriting Recognition