Publication

Efficient Exploration of Text Regions in Natural Scene Images Using Adaptive Image Sampling

By Ismet Zeki Yalniz, Douglas Gray, R. Manmatha
2016
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Abstract
An adaptive image sampling framework is proposed for identifying text regions in natural scene images. A small fraction of the pixels actually correspond to text regions. It is desirable to eliminate non-text regions at the early stages of text detection. First, the image is sampled row-by-row at a specific rate and each row is tested for containing text using an 1D adaptation of the Maximally Stable Extremal Regions (MSER) algorithm. The surrounding rows of the image are recursively sampled at finer rates to fully contain the text. The adaptive sampling process is performed on the vertical dimension as well for the identified regions. The final output is a binary mask which can be used for text detection and/or recognition purposes. The experiments on the ICDAR’03 dataset show that the proposed approach is up to 7x faster than the MSER baseline on a single CPU core with comparable text localization scores. The approach is inherently parallelizable for further speed improvements.
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Amazon music offerings are available in multiple countries, and our applications support our mission of delivering music to customers in a way that enhances their day-to-day lives. We can be found on platforms such as the Amazon Echo, Kindle Fire, iOS, and Android as well as on a mixture of home and auto streaming platforms.The Amazon Music Catalog team is responsible for persisting, computing, reconciling and vending Music rights and metadata to other teams and services across Amazon. We process hundreds of millions of updates per day and our services serve tens of thousands of requests per secs to all Amazon Music Customers. We own critical platforms that makes access to the Amazon Music Catalog highly available and accessible to every one.As a research scientist, you will provide machine learning leadership to the team that helps accelerate the business. You will build various data and machine learning models that help us innovate different ways to enhance customer experience.You will need to be entrepreneurial, wear many hats, and work in a highly collaborative environment. We like to move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality.Responsibilities:· Drive collaborative research and creative problem solving· Constructively critique peer research and mentor junior scientists and engineers· Create experiments and prototype implementations of new learning algorithms and prediction techniques· Collaborate with engineering teams to design and implement software solutions for science problems· Contribute to progress of the Amazon and broader research communities by producing publications
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Research Scientist I - AMZ4752
US, MA, Cambridge
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services, LLCJob Title: Research Scientist ILocation: Cambridge, MAPosition Responsibilities:Participate in the design, development, and evaluation of models and ML (machine learning) technology. Lay the foundation to move from directed interactions to learned behaviors that enable Alexa to proactively take action on behalf of the customer. Interact with various software and business groups to develop an understanding of their business requirements and operational processes. Create computer simulations to support operational decision-making. Identify areas with potential for improvement and work with internal teams to generate requirements to realize improvements. Design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. Create software prototypes to verify and validate the devised solutions methodologies. Integrate prototypes into production systems using standard software development tools and methodologies.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
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