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At data pro ,we provide final year projects with artificial intelligence for computer science students in Hyderabad, Visakhapatnam.
Abstract
Recognizing text in natural images can be a useful tool for image understanding using artificial intelligence. We focus on the detection problem, which is to find regions in an image occupied by text. We consider multi-layered convolutional neural networks as a means to classify local regions as both text or not, and take a sliding-window approach to scan a full image. For training we generate large synthetic datasets to complement the much smaller available sets of labelled natural images.
INTRODUCTION
Though not a recent invention, we now see a cornucopia of CNN-based models achieving state-of-the-art results in classification, localisation, semantic segmentation, and action recognition tasks, amongst others. so A desirable property of a system which is able to reason about images is to disentangle object pose and part deformation from texture and shape. thus The introduction of local max-pooling layers in CNNs has helped to satisfy this property by allowing a network to be somewhat spatially invariant to the position of features.
ALGORITHM
In traditional image processing field, rotational invariance or scale invariance is of great importance, and actually, there are many feature descriptors such SIFT and SURF famous for their consistent performance against affine transformation. In the era of Deep Learning, we tend to trust neural network could handle everything for us automatically, but I figure it is the point why a considerate amount of people don’t value this method. Objectively, the design of CNN could be insensitive to some slight rotation or translation transformation. For example, pooling layer, could tolerate the pixel switch inside the pooling window.
