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In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to build a computational model to automatically predict human perceived image quality without a reference image and without knowing the distortion present in the image. Previous(More)
In document images, we often find printed lines overlapping with hand written elements especially in case of signatures. Typical examples of such images are bank cheques and payment slips. Although the detection and removal of the horizontal lines has been addressed, the restoration of the handwritten area after removal of lines, persists to be a problem of(More)
—This paper presents a Convolutional Neural Network (CNN) for document image classification. In particular, document image classes are defined by the structural similarity. Previous approaches rely on hand-crafted features for capturing structural information. In contrast, we propose to learn features from raw image pixels using CNN. The use of CNN is(More)
—In this paper, we present a learning based approach for computing structural similarities among document images for unsupervised exploration in large document collections. The approach is based on multiple levels of content and structure. At a local level, a bag-of-visual words based on SURF features provides an effective way of computing content(More)
This paper presents a novel approach to defining document image structural similarity for the applications of classification and retrieval. We first build a codebook of SURF descriptors extracted from a set of representative training images. We then encode each document and model the spatial relationships between them by recursively partitioning the image(More)
—With the proliferation of cameras on mobile devices there is an increased desire to image document pages as an alternative to scanning. However, the quality of captured document images is often lower than its scanned equivalent due to hardware limitations and stability issues. In this context, automatic assessment of the quality of captured images is(More)
In this paper we present a novel method for removing page rule lines in monochromatic handwritten Arabic documents using subspace methods with minimal effect on the quality of the foreground text. We use moment and histogram properties to extract features that represent the characteristics of the underlying rule lines. A linear sub-space is incrementally(More)
In this paper, we present a novel graph-based method for extracting handwritten text lines in monochromatic Arabic document images. Our approach consists of two steps - Coarse text line estimation using primary components which define the line and assignment of diacritic components which are more difficult to associate with a given line. We first estimate(More)
In this paper, we present a novel method for extracting handwritten and printed text zones from noisy document images with mixed content. We use Triple-Adjacent-Segment (TAS) based features which encode local shape characteristics of text in a consistent manner. We first construct two codebooks of the shape features extracted from a set of handwritten and(More)
This paper addresses the problem of general-purpose No-Reference Image Quality Assessment (NR-IQA) with the goal of developing a real-time, cross-domain model that can predict the quality of distorted images without prior knowledge of non-distorted reference images and types of distortions present in these images. The contributions of our work are twofold:(More)