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ABBYY 3A PARTNER SUMMIT © ABBYY Confidential The Theory of Everything... in Documents Processing Oleg Sazhin, PhD, MBA ABBYY 3A
ABBYY 3A PARTNER SUMMIT© ABBYY Confidential
The Theory of Everything... in Documents Processing
Oleg Sazhin, PhD, MBA ABBYY 3A
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Theory
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About 30% of revenue is invested in R&D, ~1.5 times more than the market average
More than 500 engineers work in ABBYY’s R&D,
one of the largest research centers in Artificial intelligence
Founded: Department of Image Recognition & Text Processing, 2*Computational Linguistics Departments
Resident of Skolkovo Innovation Center
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Leveraging SynergiesABBYY develops technologies in 4 key areas:
Text recognition
Document analysis
Lexicography (dictionary creation)
Natural language
processing
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Black box
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Paper Image DATA
ECM,DMS,CRM,
DATABASE
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Self learning classificatrion. OCR/ICR/OMR
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Image-based classificationAnalyzing the look. Self learning.
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Page analysis
detects zones that contain text, pictures, and tables.
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Text blocks are parsed into lines
Vertical histogram is used
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Horizontal histogram is used• larger gaps assumed to be spaces between words • smaller gaps interpreted as spaces between letters.
Lines are parsed into words, and words into letters
Structural classifier
Differentiating classifier
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OCR – Optical character recognition202 languages, including:•European languages (Latin, Cyrillic, Armenian, Greek alphabets)
•CJK: Chinese (Simplified and Traditional), Japanese, and Korean
•Thai, Vietnamese, Hebrew, Burmese•Arabic
ICR – Intelligent character recognition136 languages
OMR – Optical mark recognition
BCR – Barcode recognition
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Mobile
Recognition from Photo
Real Time Recognition from Video stream
Augmented Reality
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Fuzzy Logic & Hypothesis trees
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Fuzzy Logic
Hypotheses are characterized by their quality (value from 0 to 1)
How long is the address filed
O or 1No Yes
O…….1
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Fuzzy Logic & Hypothesis trees
n ri a 50% 35% 15%
Q=0,5 Q=0,35
Q=0,09 Q=1
In dictionary: Cambridge
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Semantics - understanding natural language
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Semantics(in the form a language-independent hierarchy of concepts)
While people speak different languages they think using largely identical concepts.
Relationships among these concepts are the same in all languages and they can be
pictured as a conceptual tree, with the thicker boughs representing more general
notions (e.g. “furniture”) and the thinner twigs representing more specific notions
(e.g. “table”).
Understandingnatural language
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Syntax
The syntax component detects how concepts are related to one another within
one or more sentences. The system analyzes a texts and builds a tree of syntactic
relations.
Morphology is essential for this analysis.
Joint use of the above
components enables
the system to “understand”
Understanding natural language
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Understanding natural language
Resolving ambiguities:
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Semantic Exploration – How it Works• Broadening
Instead of searching for
search for the common meaning of all these words
• Narrowing
Allows searching for a topic with a restricted meaning
Finds all documents where this word appears with any meaning.
„layoff“ OR „terminate“ OR „redundant“ OR „fire“
(TO_CAUSE_TO_LEAVE)
„apple“
apple(COMPANY_BY_NAME)
Understanding natural language
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Info extraction:who, when, how much etc
Automatic tagging
Contract ManagementComplianceRecords Management Information GovernanceRisk Mitigation
Intelligent searchApple as a fruit
Smart Classification:
Oil for food
or
Oil for the car
Understanding natural language
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Convolutional Neural Networks
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Convolutional Neural Networks
from "Neural Networks and Deep Learning“ by Michael Nielsen
FilterFilter
/ feature map
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FilterMouse part
Convolutional Neural Networks
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6600
6600
Mouse part Filter
Convolutional Neural Networks
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Filter 1 Filter 2 Filter 3 Filter 4
Back Propagation
Convolutional Neural Networks
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for:- image preprocessing- documents classification- text/image/table blocks searching- OCR- RTR (mobile real time recognition)- barcodes searching- Certain lines searching (on receipts, invoices etc)etc
Convolutional Neural Networks
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2016-17 World Wide Document Capture Software
Leading Capture Software Vendors Report
By Harvey Spencer, Mike Spang, Ken Chin
Harvey Spencer Associates Inc.
“A major source of ABBYY's strength lies in its R&D ….
Russian developed neural networks and other recognition algorithms
are widely acknowledged for their strength and accuracy.”
“A FlexiCapture Distributed system can be used to build sophisticated transaction capture solutions …”
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