الفهرس 
AbstractOnly 14 pages are availabe for public view
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Abstract
Unconstrained Text Recognition is an important Computer Vision Task, featuring a wide variety of different Sub-tasks, each with its own set of challenges. One of the biggest promises of deep natural networks has been the convergence and automation of future extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a date-efficient, end to end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy.
Our proposed architecture is a fully convolutional neural network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input size and produce strings of arbitrary length in a very efficient and parallelizable manner.