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ABBYY
Technology
Summit
2017
© ABBYY Confidential
ABBYY NAHQ, 2017
FlexiCapture Technical Track
ABBYY
Technology
Summit
2017
Machine
Learning
ABBYY NAHQ, 2017
Chip VonBurg
© ABBYY Confidential
Cloud
© ABBYY Confidential 3
Web
© ABBYY Confidential 4
“Laser”
© ABBYY Confidential 5
Agenda
• What is machine learning?
• Machine Learning Algorithms and Scenario's
• Machine learning in FlexiCapture
• Q&A
©...
© ABBYY Confidential
What is Machine Learning
What is Machine Learning
• Machine learning is a method of data analysis that automates
analytical model building
• Using ...
What is Machine Learning
Machine Learning enables applications to execute
logic that wasn’t explicitly built
© ABBYY Confi...
What is Machine Learning
• How is machine learning used?
– Biology/Genetics
– Search and recommendations
• Music Services ...
What is Machine Learning
Machine Learning requires a few basic items:
• Sample Data
• An iterative process
• Machine Learn...
The structure of a typical Machine Learning
Process
© ABBYY Confidential 12
Lets see an example:
http://regex.inginf.units.it/
© ABBYY Confidential
© ABBYY Confidential
Machine Learning
Algorithms and Scenarios
Naive Bayes
• Bayes Theorem was developed in 1700’s by Thomas Bayes
– Provides a means for directly calculating the probab...
Support Vector Machine (SVM)
• SVM are supervised learning models with associated learning algorithms that analyze data
us...
Deep learning
• Deep learning is a form of a Neural Network with a bias
• Learning can be supervised, partially supervised...
Machine Learning Scenarios
• Supervised Learning (on labeled data)
– The main learning scenario. It requires manually labe...
Machine Learning Practical Examples
Sort these documents into like categories:
• Here are examples of each category
– Exam...
Machine Learning Practical Examples
Sort these documents into like categories:
• You figure out what those categories are
...
Machine Learning Practical Examples
Sort these documents into like categories:
• Here is feedback on correct or incorrect ...
© ABBYY Confidential
Machine Learning in FlexiCapture
Machine Learning in FlexiCapture
Classification
© ABBYY Confidential 23
Machine Learning in FlexiCapture
Classification
© ABBYY Confidential 24
Machine Learning in FlexiCapture
Field Training
© ABBYY Confidential 25
Machine Learning in FlexiCapture
Field Training
© ABBYY Confidential 26
INPUT
RECOGNITION
VERIFICATION
AUTOMATIC
PROCESSIN...
Machine Learning at ABBYY
• In addition to product facing changes, Machine Learning
processes continue to help ABBYY produ...
Why do we care?
© ABBYY Confidential 28
automatic
ProjectStart
Development
Production
Value
Previous Approach
With Machine...
Summary
• Machine learning is a analytical automated approach to pattern
matching
• Machine learning can be used to augmen...
Questions?
© ABBYY Confidential
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ABBYY FlexiCapture 12: Machine Learning at #ABBYYSummit17

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Machine Learning has become an increasingly popular topic in the rise of AI in today’s business landscape. This session will discuss how machine learning has been implemented with FlexiCapture 12. Not only is it a cool feature and competitive advantage for FlexiCapture, but makes for a very compelling customer demonstration when they see the system learning and improving based upon feedback and training.

Speaker: Chip VonBurg, Senior Solutions Architect, ABBYY

Published in: Technology
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ABBYY FlexiCapture 12: Machine Learning at #ABBYYSummit17

  1. 1. ABBYY Technology Summit 2017 © ABBYY Confidential ABBYY NAHQ, 2017 FlexiCapture Technical Track
  2. 2. ABBYY Technology Summit 2017 Machine Learning ABBYY NAHQ, 2017 Chip VonBurg © ABBYY Confidential
  3. 3. Cloud © ABBYY Confidential 3
  4. 4. Web © ABBYY Confidential 4
  5. 5. “Laser” © ABBYY Confidential 5
  6. 6. Agenda • What is machine learning? • Machine Learning Algorithms and Scenario's • Machine learning in FlexiCapture • Q&A © ABBYY Confidential 6
  7. 7. © ABBYY Confidential What is Machine Learning
  8. 8. What is Machine Learning • Machine learning is a method of data analysis that automates analytical model building • Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look • Evolved from the study of pattern recognition and computational learning theory • Machine learning is closely related to (and often overlaps with) computational statistics Confidential 8
  9. 9. What is Machine Learning Machine Learning enables applications to execute logic that wasn’t explicitly built © ABBYY Confidential 9
  10. 10. What is Machine Learning • How is machine learning used? – Biology/Genetics – Search and recommendations • Music Services (song/station suggestions) • Movie Services (Netflix/Amazon, etc) – Thumbs Up/Down is an example of Reinforcement learning and a feedback loop • Add Services – SPAM detection – Anywhere Pattern matching can be applied © ABBYY Confidential 10
  11. 11. What is Machine Learning Machine Learning requires a few basic items: • Sample Data • An iterative process • Machine Learning Algorithms – Naive Bayes – Support Vector Machine (SVM) – Genetic Algorithms – Deep learning Confidential 11
  12. 12. The structure of a typical Machine Learning Process © ABBYY Confidential 12
  13. 13. Lets see an example: http://regex.inginf.units.it/ © ABBYY Confidential
  14. 14. © ABBYY Confidential Machine Learning Algorithms and Scenarios
  15. 15. Naive Bayes • Bayes Theorem was developed in 1700’s by Thomas Bayes – Provides a means for directly calculating the probability of a statement being true based on the available evidence • Naive Bayes classifier is a simple probabilistic classifiers applying Bayes' theorem with strong (naive) independence assumptions between the features • Does an item belong or not belong based on the features within it? • Benefits of Naive Bayes classifiers – Naive Bayes classifiers can be taught very effectively depending on the exact nature of the probabilistic model. – Despite their very simplistic terms, Naive Bayes classifiers often work well in many complex tasks. – The advantage of the Naive Bayes classifier is the small amount of training data needed. Confidential 15
  16. 16. Support Vector Machine (SVM) • SVM are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis • Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other • In addition to performing linear classification, SVMs can efficiently perform a non-linear classification • The method is widely used in virtually all tasks. It is considered a basic method; more complex methods are used only if they provide a significant advantage over the support vector machine. Confidential 16
  17. 17. Deep learning • Deep learning is a form of a Neural Network with a bias • Learning can be supervised, partially supervised or unsupervised. • Some representations are loosely based on interpretation of information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain. • Have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts © ABBYY Confidential 17
  18. 18. Machine Learning Scenarios • Supervised Learning (on labeled data) – The main learning scenario. It requires manually labeled data. This is useful for tasks that do not require a lot of labeled data or where such data emerge naturally. • Unsupervised Learning (on unlabeled data) – This scenario makes it possible to find patterns in large arrays of raw data (big data analysis). It can be used in combination with supervised learning in order to significantly reduce the amount of manual labeling. • Reinforcement learning (by correcting errors on the fly) – The scenario allows to improve results during system operation, including via interaction with the user Confidential 18
  19. 19. Machine Learning Practical Examples Sort these documents into like categories: • Here are examples of each category – Example of supervised learning – Example of standard training process used for classification or extraction – Algorithm must determine what features all of the included documents have in common that excluded documents don’t have Confidential 19
  20. 20. Machine Learning Practical Examples Sort these documents into like categories: • You figure out what those categories are – Example of unsupervised learning – Example of Clustering – Algorithm must determine how to group objects based on like features Confidential 20
  21. 21. Machine Learning Practical Examples Sort these documents into like categories: • Here is feedback on correct or incorrect choices that have been made – Example of Reinforcement learning – Example of a “Feedback Loop” – Allows for the refinement of the model through information from the user Confidential 21
  22. 22. © ABBYY Confidential Machine Learning in FlexiCapture
  23. 23. Machine Learning in FlexiCapture Classification © ABBYY Confidential 23
  24. 24. Machine Learning in FlexiCapture Classification © ABBYY Confidential 24
  25. 25. Machine Learning in FlexiCapture Field Training © ABBYY Confidential 25
  26. 26. Machine Learning in FlexiCapture Field Training © ABBYY Confidential 26 INPUT RECOGNITION VERIFICATION AUTOMATIC PROCESSING EXPORT AUTO-LEARNING
  27. 27. Machine Learning at ABBYY • In addition to product facing changes, Machine Learning processes continue to help ABBYY produce and refine technology internally: – Real world classifiers – Recognition engine training – NLP Model training – Etc © ABBYY Confidential 27
  28. 28. Why do we care? © ABBYY Confidential 28 automatic ProjectStart Development Production Value Previous Approach With Machine Learning
  29. 29. Summary • Machine learning is a analytical automated approach to pattern matching • Machine learning can be used to augment and improve traditional rule based processes • Machine learning helps flip flop the traditional development cycle and speed projects to value © ABBYY Confidential 29
  30. 30. Questions? © ABBYY Confidential

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