Dejan Depalov

Binary image compression using conditional entropy-based dictionary design and indexing

The JBIG2 standard is widely used for binary document image compression primarily because it achieves much higher compression ratios than conventional facsimile encoding standards, such as T.4, T.6, and T.82 (JBIG1). A typical JBIG2 encoder works by first separating the document into connected components, or symbols. Next it creates a dictionary by encoding a subset of symbols from the image, and finally it encodes all the remaining symbols using the dictionary entries as a reference. In this paper, we propose a novel method for measuring the distance between symbols based on a conditionalentropy estimation (CEE) distance measure. The CEE distance measure is used to both index entries of the dictionary and construct the dictionary. The advantage of the CEE distance measure, as compared to conventional measures of symbol similarity, is that the CEE provides a much more accurate estimate of the number of bits required to encode a symbol. In experiments on a variety of documents, we demonstrate that the incorporation of the CEE distance measure results in approximately a 14% reduction in the overall bitrate of the JBIG2 encoded bitstream as compared to the best conventional dissimilarity measures.

Dynamic hierarchical dictionary design for multi-page binary document image compression

The JBIG2 standard is widely used for binary document image compression primarily because it achieves much higher compression ratios than conventional facsimile encoding standards. In this paper, we propose a dynamic hierarchical dictionary design method (DH) for multi-page binary document image compression with JBIG2. Our DH method outperforms other methods for multi-page compression by utilizing the information redundancy among pages with the following technologies. First, we build a hierarchical dictionary to keep more information per page for future usage. Second, we dynamically update the dictionary in memory to keep as much information as possible subject to the memory constraint. Third, we incorporate our conditional entropy estimation algorithm to utilize the saved information more effectively. Our experimental results show that the compression ratio improvement by our DH method is about 15% compared to the best existing multi-page encoding method.

Text, photo, and line extraction in scanned documents

Abstract. We propose a page layout analysis algorithm to classify a scanned document into different regions such as text, photo, or strong lines. The proposed scheme consists of five modules. The first module performs several image preprocessing techniques such as image scaling, filtering, color space conversion, and gamma correction to enhance the scanned image quality and reduce the computation time in later stages. Text detection is applied in the second module wherein wavelet transform and run-length encoding are employed to generate and validate text regions, respectively. The third module uses a Markov random field based block-wise segmentation that employs a basis vector projection technique with maximum a posteriori probability optimization to detect photo regions. In the fourth module, methods for edge detection, edge linking, line-segment fitting, and Hough transform are utilized to detect strong edges and lines. In the last module, the resultant text, photo, and edge maps are combined to generate a page layout map using K-Means clustering. The proposed algorithm has been tested on several hundred documents that contain simple and complex page layout structures and contents such as articles, magazines, business cards, dictionaries, and newsletters, and compared against state-of-the-art page-segmentation techniques with benchmark performance. The results indicate that our methodology achieves an average of ∼89% classification accuracy in text, photo, and background regions.

Document image binarization via one-pass local classification

Binarization algorithms are used to create a binary representation of a raster document image, typically with the intent of identifying text and separating it from background content. In this paper, we propose a binarization algorithm via one-pass local classification. The algorithm first generates the initial binarization results by local thresholding, then corrects the results by a one-pass local classification strategy, followed by the process of component inversion. The experimental results demonstrate that our algorithm achieves a somewhat lower binarization error rate than the state-of-the-art algorithm COS [1], while requiring significantly less computation.

A color quantization algorithm based on minimization of modified Lp norm error in a CIELAB space

Color quantization algorithms are used to select a small number of colors that can accurately represent the content of a particular image. In this research, we introduce a novel color quantization algorithm which is based on the minimization of a modified Lp norm rather than the more traditional L2 norm associated with mean square error (MSE). We demonstrate that the Lp optimization approach has two advantages. First, it distributes the colors more uniformly over the regions of the image; and second, the norms value can be used as an effective criterion for selecting the minimum number of colors necessary to achieve accurate representation of the image. One potential disadvantage of the modified Lp norm criteria is that it could increase the computation of the associated clustering methods. However, we solve this problem by introducing a two stage clustering procedure in which the first stage (pre-clustering) agglomerates the full set of pixels into a relatively large number of discrete colors; and the second stage (post-clustering) performs modified Lp norm minimization using the reduced number of discrete colors resulting from the pre-clustering step. The number of groups used in the post-clustering is then chosen to be the smallest number that achieves a selected threshold value of the normalized Lp norm. This two-stage clustering process dramatically reduces computation by merging together colors before the computationally expensive modified Lp norm minimization is applied.

Analysis and classification for complex scanned documents

Page-layout-classification methodologies aim to extract text and non-textual regions such as graphics, photos, or logos. These techniques have applications in digital document storage and retrieval where efficient memory consumption and quick retrieval are required.1 Such classification algorithms can also be used in the printing industry for selective or enhanced scanning and object-oriented rendering (printing different parts of a document with different resolution depending on the content).2 Additionally, these techniques can be used as an initial step for various applications. These include optical-character recognition (the electronic translation of handwritten or printed text into machine-encoded text) and graphic interpretation (classifying documents—into military, educational, and others—according to the image content).3 In the past two decades, several techniques have focused on identifying text regions in scanned documents.4, 5 In addition, comprehensive algorithms that aim to identify both text and graphic regions have been developed.6, 7 However, these systems are limited to specific documents, such as newsletters or articles, where the background region is assumed white.8, 9 This assumption not only excludes complex backgrounds and colored documents (such as book covers, advertisements, and flyers),10 but also limits practicality and feasibility when applied to non-ideal (complex) documents. We propose a page-layout-segmentation technique to extract text, image, and strong-edge or strong-line regions (actual lines in the document or transition pixels between a picture and text or a picture and background).11 The algorithm consists of four modules: pre-processing stage, text detection, photo detection, and strong-edge or strong-line detection units. We start by applying a pre-processing module that includes image scaling and enhancement, as well as color-space conversion Figure 1. Line detection results for two different documents. (a) Original image, (b) enhanced L* channel of the CIE L*a*b* space, and (c) final segmentation map where strong-edge or strong-line and text regions are colored in yellow and green, respectively.