MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Mukhopadhyay%20-%20Hough%20Transform%20-%202014.pdf

A survey of Hough Transform

For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.

https://link.springer.com/content/pdf/10.1007%2Fs13735-012-0004-6.pdf

Optical music recognition: state-of-the-art and open issues

AdOtsu: An adaptive and parameterless generalization of Otsu's method for document image binarization

A multi-scale framework for adaptive binarization of degraded document images

Historical document collections are a valuable resource for human history. This paper proposes a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. The proposed scheme consists of five distinct steps: a pre-processing procedure using a low-pass Wiener filter, a rough estimation of foreground regions using Niblack’s approach, a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image and finally a post-processing step in order to improve the quality of text regions and preserve stroke connectivity. The proposed methodology works with great success even in cases of historical manuscripts with poor quality, shadows, nonuniform illumination, low contrast, large signal- dependent noise, smear and strain. After testing the proposed method on numerous low quality historical manuscripts, it has turned out that our methodology performs better compared to current state-of-the-art adaptive thresholding techniques.

/pdf/an-adaptive-binarization-technique-for-low-quality-38tb9jvlqy.pdf

An Adaptive Binarization Technique for Low Quality Historical Documents

In this paper, we propose a novel technique for automatic table detection in document images. Lines and tables are among the most frequent graphic, non-textual entities in documents and their detection is directly related to the OCR performance as well as to the document layout description. We propose a workflow for table detection that comprises three distinct steps: (i) image pre-processing; (ii) horizontal and vertical line detection and (iii) table detection. The efficiency of the proposed method is demonstrated by using a performance evaluation scheme which considers a great variety of documents such as forms, newspapers/magazines, scientific journals, tickets/bank cheques, certificates and handwritten documents.

/pdf/automatic-table-detection-in-document-images-50bwaysvjj.pdf

Automatic table detection in document images

http://www.ipet.gr/~chamzas/chamzas_pdfs/publications/199709_elsevier.pdf

Skew detection and text line position determination in digitized documents

http://biorobotics.ri.cmu.edu/papers/sbp_papers/integrated2/perantonis_block_decomp_hough.pdf

Block decomposition and segmentation for fast Hough transform evaluation

A novel fast method for evaluating the Hough transform is proposed, which can be used to accelerate detection of prevalent linear formations in binary images. An image is decomposed using rectangular blocks and the contribution of each whole block to the Hough transform space is evaluated, rather than the contribution of each image point. The resulting acceleration in the calculation of the Hough transform field is demonstrated in two image processing experiments related to object axis identification and skew detection of digitised documents.

Basilios Gatos

Papers

An Adaptive Binarization Technique for Low Quality Historical Documents

Automatic table detection in document images

Skew detection and text line position determination in digitized documents

Block decomposition and segmentation for fast Hough transform evaluation

Accelerated Hough transform using rectangular image decomposition