Optical character recognition (OCR) technology can take your magazine article, brochure, or PDF, single out letters on the image, and put them into words, enabling you to access and edit the content. Similarly, facial recognition software breaks down an image and can provide data related to gender, age, and ethnicity. Couple these with interior location technology, and you have the potential for powerful organizational learning opportunities. In this session, you will explore an in-depth case study on an augmented reality (AR) application that handles OCR, facial detection, and microlocation (interior) wayfinding. You will learn the basics of image processing and the difference between off-the-shelf and custom AR solutions. Finally, you will experience a hardware and software demonstration and get a chance to try out the solution. In this session, attendees learned: How advanced AR development differs from off-the-shelf solutions About the intricacies of crafting custom AR About basic image processing About advanced custom AR software through a demonstration Audience: Intermediate and advanced designers, developers, and directors. Technology discussed in this session: Augmented reality, optical character recognition, numerical masks, facial detection, and local wayfinding.

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Enterprise-Grade AR for
Operational Environments:
A Postmortem

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Advanced Augmented Reality: OCR Case
Study on Facial Detection and More

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About Chad Udell
Managing Director at Float
Faculty for over 10 years at Bradley University in Interactive Media
Been designing and developing web and learning solutions for nearly 20 years
A bit of a hacker/tinkerer, and a learning technology and design geek
chadu@gowithfloat.com - @visualrinse on Twitter
Author of Learning Everywhere: How Mobile Content Strategies are Transforming Training,
Editor and Author of Mastering Mobile Learning: Tips and Techniques for Success

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AR Technolust.
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AR in Common Use.
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http://adage.com/article/cat-creativity-and-technology/ge-
takes-super-bowl-holograms-smart-grid-push/134229/

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https://www.youtube.com/watch?v=RWwQXi9RG0w

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https://www.youtube.com/watch?v=9ZiBbghbbRQ

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https://www.youtube.com/watch?v=uqQqn7RWffE

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What are the obstacles?
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What are the obstacles?
• Technology Availability
• Price
• True Use Case Identification
• Risk
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So, what did we propose?
• Technology Availability
• Only using commercially available products
• Price
• Make sure hardware was something that
departments or agencies could purchase
• True Use Case Identification
• All use cases needed detailed stories and personas
created
• Risk
• Research Project with Plans for Transition and
Release Based on Outcomes
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Float’s Experience.
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http://2015.icdar.org/

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Hardware.
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Epson Moverio BT-200
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Osterhout Design Group R-6
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Lessons Learned.
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Face Detection
• Open CV
• Android
- android.hardware.Camera.startFaceDetection
- Deprecated in API level 21
- Requires Android camera access
- Unity compatibility issues
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Face Recognition
• Microsoft Project Oxford Face API
- how-old.net
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Text Detection
• Commercial solutions
• WordLens & PhotoOCR
• Tesseract
• tess-two
• Otsu thresholding
Bissacco, A., Cummins, M., Netzer, Y., Neven, H. (2013).
PhotoOCR: Reading text in uncontrolled conditions. Google, Inc.
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Text Detection
• Commercial solutions
• WordLens & PhotoOCR
• Tesseract
• tess-two
• Otsu thresholding
• Stroke width transform
• libccv
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Location Tracking
• Indoor location
• GPS unavailable
• Sensor fusion
- Bluetooth
- Wi-Fi
- Gravitometer
- Magnetometer
• Area learning
• Model generation
- Tango
- Robots
• Bluetooth
Image credit: Google Project Tango
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User Interface
• Stereoscopy
- Platform SDKs
• Cardboard SDK
- Drift
- Tango SDK Compatibility
• Color choice
- Fonts
• Intent
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Build Process
• Gradle
• AAR
• Editor plugins
• Automate
• Time to build
- Maintaining flow
- Iterating
• Test
• Demos
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Q&A.
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Thank you!