In this paper, we describe an efficient pipeline for real-time text detection to be implemented on different architectures, with particular reference to smart phones. The text detection pipeline is based on a rather standard segmentation followed by a classification of each segmented connected component. Segmentation is performed by a linear implementation of MSER, state-of-the-art for text detection, where we control the overall computational cost of the method by computing a set of descriptive features as segmentation goes on. Classification is carried out by a cascade of SVM classifiers, where each layer captures different levels of complexity by means of an appropriate choice of descriptive features and kernel functions. Each detected text element, or character, is finally merged into lines of text and words. Further on, each element can be fed to a multi-class classifier that performs character recognition—this functionality is currently under development. We report experiments aiming at assessing the appropriateness of the text detection procedure, in terms of both performance and speed, when running on both x86 and ARM processors.
|Titolo:||Portable and fast text detection|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||01.01 - Articolo su rivista|
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