Abstract-Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been proposed to design better feature representations and models for both. In this paper, we apply methods recently developed in machine learning-specifically, large-scale algorithms for learning the features automatically from unlabeled data-and show that they allow us to construct highly effective classifiers for both detection and recognition to be used in a high accuracy end-to-end system.

Abstract-We consider the problem of automatically collecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules. In particular, we describe a system by which a SLAMgenerated map of an office environment can be annotated with text labels such as room numbers and the names of office occupants. These labels are acquired automatically from signs posted on walls throughout a building. Deploying such a system using current text recognition systems, however, is difficult since even state-of-the-art systems have difficulty reading text from non-document images. Despite these difficulties we present a series of additions to the typical mapping pipeline that nevertheless allow us to create highly usable results. In fact, we show how our text detection and recognition system, combined with several other ingredients, allows us to generate an annotated map that enables our robot to recognize named locations specified by a user in 84% of cases.

Image restoration is the process of renovating a corrupted/noisy image for obtaining a clean original image. Numerous MRF based restoration methods were utilized for performing image restoration process. In such works, there is a lack of analysis in selecting the top similar local patches and Gaussian noise images. Hence, in this paper, a heuristic image restoration technique is proposed to obtain the noise free images. The proposed heuristic image restoration technique is composed of two steps: core processing and post processing. In core processing, the local and global features of each pixel values of the noisy image are extracted and restored the noise free pixel value by exploiting the extracted features and Markov Random Field (MRF). Moreover, the restored image quality and boundary edges are sharpened by the post processing function. The implementation result shows the effectiveness of proposed heuristic technique in restoring the noisy images. The performance of the image restoration technique is evaluated by comparing its result with the existing image restoration technique. The comparison result shows a high-quality restoration ratio for the noisy images than the existing restoration ratio, in terms of peak signal-to-noise ratio (PSNR).

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