Recognition of Street View Images and languages and converting it into user understandable format

Abstract

Camera-enabled mobile devices(Android) are commonly used as interaction platforms for linking the users
virtual and physical worlds in commercial applications and numerous research, like serving an augmented reality
interface. (OPSs) On-premise signs, a popular form of commercial advertising, are widely used in our living life. The
OPSs usually exhibit great visual diversity, accompanied with complex environmental terms(conditions) (e.g.,
foreground and back- ground clutter). The language barrier among tourists is one of the major difficulty when traveling.
The tourists can depend on mobile phone for traveling purposes.
Android provides a translation platform for this base, performing 2 times faster than other algorithms. We first proposed
an OPS data set, in which images of different businesses are collected from Googles Street View. Further, for addressing
the problem of real-world OPS learning then recognition, we developed a probabilistic framework based on the
distributional clustering, in which we proposed to exploit the distributional information of each visual feature as a
reliable selection criterion for building discriminate OPS models. Our approach is linear, simple and can be
executed in a parallel fashion, making it scalable and practical for large-scale multimedia applications.
Index Terms- world objects, street view scenes, object image data set, Pre-process, OPS, Smart phone, Text Extraction,
Mobile platform, Segmentation, Translator