Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

Techniques for document image processing in compressed domain

The main objective for image compression is usually considered the minimization of storage space. However, as the need to frequently access images increases, it is becoming more important for people to process the compressed representation directly. In this work, the techniques that can be applied directly and efficiently to digital information encoded by a given compression algorithm are investigated. Lossless compression schemes and information processing algorithms for binary document images and text data are two closely related areas bridged together by the fast processing of coded data. The compressed domains, which have been addressed in this work, i.e., the ITU fax standards and JBIG standard, are two major schemes used for document compression. Based on ITU Group IV, a modified coding scheme, MG4, which explores the 2-dimensional correlation between scan lines, is developed. From the viewpoints of compression efficiency and processing flexibility of image operations, the MG4 coding principle and its feature-preserving behavior in the compressed domain are investigated and examined. Two popular coding schemes in the area of bi-level image compression, run-length and Group IV, are studied and compared with MG4 in the three aspects of compression complexity, compression ratio, and feasibility of compressed-domain algorithms. In particular, for the operations of connected component extraction, skew detection, and rotation, MG4 shows a significant speed advantage over conventional algorithms. Some useful techniques for processing the JBIG encoded images directly in the compressed domain, or concurrently while they are being decoded, are proposed and generalized; In the second part of this work, the possibility of facilitating image processing in the wavelet transform domain is investigated. The textured images can be distinguished from each other by examining their wavelet transforms. The basic idea is that highly textured regions can be segmented using feature vectors extracted from high frequency bands based on the observation that textured images have large energies in both high and middle frequencies while images in which the grey level varies smoothly are heavily dominated by the low-frequency channels in the wavelet transform domain. As a result, a new method is developed and implemented to detect textures and abnormalities existing in document images by using polynomial wavelets. Segmentation experiments indicate that this approach is superior to other traditional methods in terms of memory space and processing time

Document image processing using irregular pyramid structure

Ph.DDOCTOR OF PHILOSOPH

Handbook of Document Image Processing and Recognition

International audienceThe Handbook of Document Image Processing and Recognition provides a consistent, comprehensive resource on the available methods and techniques in document image processing and recognition. It includes unified comparison and contrast analysis of algorithms in standard table formats. Thus, it educates the reader in order to help them to make informed decisions on their particular problems. The handbook is divided into several parts. Each part starts with an introduction written by the two editors. These introductions set the general framework for the main topic of each part and introduces the contribution of each chapter within the framework. The introductions are followed by several chapters written by established experts of the field. Each chapter provides the reader with a clear overview of the topic and of the state of the art in techniques used (including elements of comparison between them). Each chapter is structured in the same way: It starts with an introductory text, concludes with a summary of the main points addressed in the chapter and ends with a comprehensive list of references. Whenever appropriate, the authors include specific sections describing and pointing to consolidated software and/or reference datasets. Numerous cross-references between the chapters ensure this is a truly integrated work, without unnecessary duplications and overlaps between chapters. This reference work is intended for the use by a wide audience of readers from around the world such as graduate students, researchers, librarians, lecturers, professionals, and many other people

Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques

One of the most important steps of document image processing is binarization. The computational requirements of locally adaptive binarization techniques make them unsuitable for devices with limited computing facilities. In this paper, we have presented a computationally efficient implementation of convolution based locally adaptive binarization techniques keeping the performance comparable to the original implementation. The computational complexity has been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the image size. Experiments over benchmark datasets show that the computation time has been reduced by 5 to 15 times depending on the window size while memory consumption remains the same with respect to the state-of-the-art algorithmic implementation

Estimation of the Handwritten Text Skew Based on Binary Moments

Binary moments represent one of the methods for the text skew estimation in binary images. It has been used widely for the skew identification of the printed text. However, the handwritten text consists of text objects, which are characterized with different skews. Hence, the method should be adapted for the handwritten text. This is achieved with the image splitting into separate text objects made by the bounding boxes. Obtained text objects represent the isolated binary objects. The application of the moment-based method to each binary object evaluates their local text skews. Due to the accuracy, estimated skew data can be used as an input to the algorithms for the text line segmentation

Knowledge Management Applied To Business Process Reengineering

The purpose of this paper is to highlight the role of knowledge management (KM) as a critical factor for the business process reengineering (BPR) success. It supports the theory that the knowledge management can supply the dynamic necessary to stimulate successful reengineering and minimize the failure rate and its sources. Implementing KM strategy in reengineering projects will lead to better outcomes, building the support for long-term success into the design of business systems and processes