A novel method for extracting and recognizing logos

Nowadays, the high volume of archival documents has made it exigent to store documents in electronic databases. A text logo represents the ownership of the text, and different texts can be categorized by it; for this reason, different methods have been presented for extracting and recognizing logos. The methods presented earlier, suffer problems such as, error of logo detection and recognition and slow speed. The proposed method of this study is composed of three sections: In the first section, the exact position of the logo can be identified by the pyramidal tree structure and horizontal and vertical analysis, and in the second section, the logo can be extracted through the algorithm of the boundary extension of feature rectangles. In the third section, after normalizing the size of the logo and eliminating the skew angle, for feature extraction, we first blocked the region encompassing the logo, and then we extract a particular feature by the parameter of the center of gravity of connected component each block. Finally, we use the KNN classification for the recognition of the logo.DOI:http://dx.doi.org/10.11591/ijece.v2i5.129

Efficient Machine Learning Methods for Document Image Analysis

With the exponential growth in volume of multimedia content on the internet, there has been an increasing interest for developing more efficient and scalable algorithms to learn directly from data without excessive restrictions on nature of the content. In the context of document images, many large scale digitization projects have called for reliable and scalable triage methods for enhancement, segmentation, grouping and categorization of captured images. Current approaches, however, are typically limited to a specific class of documents such as scanned books, newspapers, journal articles or forms for example, and analysis and processing of more unconstrained and noisy heterogeneous document collections has not been as widely addressed. Additionally, existing machine-learning based approaches for document processing need to be carefully applied to handle the challenges associated with large and imbalanced training data. In this thesis, we address these challenges in three primary applications of document image analysis - low-level document enhancement, mid-level handwritten line segmentation, and high-level classification and retrieval. We first present a data selection method for training Support Vector Machines (SVM) on large-scale data sets. We apply the proposed approach to pixel-level document image enhancement, and show promising results with a relatively small number of training samples. Second, we present a graph-based method for segmentation of handwritten document images into text-lines which is more efficient and adaptive than previous approaches. Our approach demonstrates that combining results from local and global methods enhances the final performance of text-line segmentation. Third, we present an approach to compute structural similarities between images for classification and retrieval. Results on real-world data sets show that the approach is more effective than earlier approaches when the labeled data is limited. We extend our classification approach to a completely unsupervised setting, where both the number of classes and representative samples from each class is assumed to be unknown. We present a method for computing similarities based on learned structural patterns and correlations from the given data. Experiments with four different data sets show that our approach can estimate number of classes in large document collections and group structurally similar images with a high-accuracy

SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS

A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined

Logo matching for document image retrieval

Graphics detection and recognition are fundamental research problems in document image analysis and retrieval. As one of the most pervasive graphical elements in business and government documents, logos may enable immediate identification of organizational entities and serve extensively as a declaration of a document’s source and ownership. In this work, we developed an automatic logo-based document image retrieval system that handles: 1) Logo detection and segmentation by boosting a cascade of classifiers across multiple image scales; and 2) Logo matching using translation, scale, and rotation invariant shape descriptors and matching algorithms. Our approach is segmentation free and layout independent and we address logo retrieval in an unconstrained setting of 2-D feature point matching. Finally, we quantitatively evaluate the effectiveness of our approach using large collections of real-world complex document images. 1