Character Segmentation in Asian Collector's Seal Imprints: An Attempt to Retrieval Based on Ancient Character Typeface

Collector's seals provide important clues about the ownership of a book. They contain much information pertaining to the essential elements of ancient materials and also show the details of possession, its relation to the book, the identity of the collectors and their social status and wealth, amongst others. Asian collectors have typically used artistic ancient characters rather than modern ones to make their seals. In addition to the owner's name, several other words are used to express more profound meanings. A system that automatically recognizes these characters can help enthusiasts and professionals better understand the background information of these seals. However, there is a lack of training data and labelled images, as samples of some seals are scarce and most of them are degraded images. It is necessary to find new ways to make full use of such scarce data. While these data are available online, they do not contain information on the characters'position. The goal of this research is to provide retrieval tools assist in obtaining more information from Asian collector's seals imprints without consuming a lot of computational resources. In this paper, a character segmentation method is proposed to predict the candidate characters'area without any labelled training data that contain character coordinate information. A retrieval-based recognition system that focuses on a single character is also proposed to support seal retrieval and matching. The experimental results demonstrate that the proposed character segmentation method performs well on Asian collector's seals, with 92% of the test data being correctly segmented

A character-recognition system for Hangeul

This work presents a rule-based character-recognition system for the Korean script, Hangeul. An input raster image representing one Korean character (Hangeul syllable) is thinned down to a skeleton, and the individual lines extracted. The lines, along with information on how they are interconnected, are translated into a set of hierarchical graphs, which can be easily traversed and compared with a set of reference structures represented in the same way. Hangeul consists of consonant and vowel graphemes, which are combined into blocks representing syllables. Each reference structure describes one possible variant of such a grapheme. The reference structures that best match the structures found in the input are combined to form a full Hangeul syllable. Testing all of the 11 172 possible characters, each rendered as a 200-pixel-squared raster image using the gothic font AppleGothic Regular, had a recognition accuracy of 80.6 percent. No separation logic exists to be able to handle characters whose graphemes are overlapping or conjoined; with such characters removed from the set, thereby reducing the total number of characters to 9 352, an accuracy of 96.3 percent was reached. Hand-written characters were also recognised, to a certain degree. The work shows that it is possible to create a workable character-recognition system with reasonably simple means

Adaptive Algorithms for Automated Processing of Document Images

Large scale document digitization projects continue to motivate interesting document understanding technologies such as script and language identification, page classification, segmentation and enhancement. Typically, however, solutions are still limited to narrow domains or regular formats such as books, forms, articles or letters and operate best on clean documents scanned in a controlled environment. More general collections of heterogeneous documents challenge the basic assumptions of state-of-the-art technology regarding quality, script, content and layout. Our work explores the use of adaptive algorithms for the automated analysis of noisy and complex document collections. We first propose, implement and evaluate an adaptive clutter detection and removal technique for complex binary documents. Our distance transform based technique aims to remove irregular and independent unwanted foreground content while leaving text content untouched. The novelty of this approach is in its determination of best approximation to clutter-content boundary with text like structures. Second, we describe a page segmentation technique called Voronoi++ for complex layouts which builds upon the state-of-the-art method proposed by Kise [Kise1999]. Our approach does not assume structured text zones and is designed to handle multi-lingual text in both handwritten and printed form. Voronoi++ is a dynamically adaptive and contextually aware approach that considers components' separation features combined with Docstrum [O'Gorman1993] based angular and neighborhood features to form provisional zone hypotheses. These provisional zones are then verified based on the context built from local separation and high-level content features. Finally, our research proposes a generic model to segment and to recognize characters for any complex syllabic or non-syllabic script, using font-models. This concept is based on the fact that font files contain all the information necessary to render text and thus a model for how to decompose them. Instead of script-specific routines, this work is a step towards a generic character and recognition scheme for both Latin and non-Latin scripts

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

Visual-based decision for iterative quality enhancement in robot drawing.

Kwok, Ka Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 113-116).Abstracts in English and Chinese.ABSTRACT --- p.iChapter 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Artistic robot in western art --- p.1Chapter 1.2 --- Chinese calligraphy robot --- p.2Chapter 1.3 --- Our robot drawing system --- p.3Chapter 1.4 --- Thesis outline --- p.3Chapter 2. --- ROBOT DRAWING SYSTEM --- p.5Chapter 2.1 --- Robot drawing manipulation --- p.5Chapter 2.2 --- Input modes --- p.6Chapter 2.3 --- Visual-feedback system --- p.8Chapter 2.4 --- Footprint study setup --- p.8Chapter 2.5 --- Chapter summary --- p.10Chapter 3. --- LINE STROKE EXTRACTION AND ORDER ASSIGNMENT --- p.11Chapter 3.1 --- Skeleton-based line trajectory generation --- p.12Chapter 3.2 --- Line stroke vectorization --- p.15Chapter 3.3 --- Skeleton tangential slope evaluation using MIC --- p.16Chapter 3.4 --- Skeleton-based vectorization using Bezier curve interpolation --- p.21Chapter 3.5 --- Line stroke extraction --- p.25Chapter 3.6 --- Line stroke order assignment --- p.30Chapter 3.7 --- Chapter summary --- p.33Chapter 4. --- PROJECTIVE RECTIFICATION AND VISION-BASED CORRECTION --- p.34Chapter 4.1 --- Projective rectification --- p.34Chapter 4.2 --- Homography transformation by selected correspondences --- p.35Chapter 4.3 --- Homography transformation using GA --- p.39Chapter 4.4 --- Visual-based iterative correction example --- p.45Chapter 4.5 --- Chapter summary --- p.49Chapter 5. --- ITERATIVE ENHANCEMENT ON OFFSET EFFECT AND BRUSH THICKNESS --- p.52Chapter 5.1 --- Offset painting effect by Chinese brush pen --- p.52Chapter 5.2 --- Iterative robot drawing process --- p.53Chapter 5.3 --- Iterative line drawing experimental results --- p.56Chapter 5.4 --- Chapter summary --- p.67Chapter 6. --- GA-BASED BRUSH STROKE GENERATION --- p.68Chapter 6.1 --- Brush trajectory representation --- p.69Chapter 6.2 --- Brush stroke modeling --- p.70Chapter 6.3 --- Stroke simulation using GA --- p.72Chapter 6.4 --- Evolutionary computing results --- p.77Chapter 6.5 --- Chapter summary --- p.95Chapter 7. --- BRUSH STROKE FOOTPRINT CHARACTERIZATION --- p.96Chapter 7.1 --- Footprint video capturing --- p.97Chapter 7.2 --- Footprint image property --- p.98Chapter 7.3 --- Experimental results --- p.102Chapter 7.4 --- Chapter summary --- p.109Chapter 8. --- CONCLUSIONS AND FUTURE WORKS --- p.111BIBLIOGRAPHY --- p.11

A Survey of Geometric Analysis in Cultural Heritage

We present a review of recent techniques for performing geometric analysis in cultural heritage (CH) applications. The survey is aimed at researchers in the areas of computer graphics, computer vision and CH computing, as well as to scholars and practitioners in the CH field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives

Wholetoning: Synthesizing Abstract Black-and-White Illustrations

Black-and-white imagery is a popular and interesting depiction technique in the visual arts, in which varying tints and shades of a single colour are used. Within the realm of black-and-white images, there is a set of black-and-white illustrations that only depict salient features by ignoring details, and reduce colour to pure black and white, with no intermediate tones. These illustrations hold tremendous potential to enrich decoration, human communication and entertainment. Producing abstract black-and-white illustrations by hand relies on a time consuming and difficult process that requires both artistic talent and technical expertise. Previous work has not explored this style of illustration in much depth, and simple approaches such as thresholding are insufficient for stylization and artistic control. I use the word wholetoning to refer to illustrations that feature a high degree of shape and tone abstraction. In this thesis, I explore computer algorithms for generating wholetoned illustrations. First, I offer a general-purpose framework, “artistic thresholding”, to control the generation of wholetoned illustrations in an intuitive way. The basic artistic thresholding algorithm is an optimization framework based on simulated annealing to get the final bi-level result. I design an extensible objective function from our observations of a lot of wholetoned images. The objective function is a weighted sum over terms that encode features common to wholetoned illustrations. Based on the framework, I then explore two specific wholetoned styles: papercutting and representational calligraphy. I define a paper-cut design as a wholetoned image with connectivity constraints that ensure that it can be cut out from only one piece of paper. My computer generated papercutting technique can convert an original wholetoned image into a paper-cut design. It can also synthesize stylized and geometric patterns often found in traditional designs. Representational calligraphy is defined as a wholetoned image with the constraint that all depiction elements must be letters. The procedure of generating representational calligraphy designs is formalized as a “calligraphic packing” problem. I provide a semi-automatic technique that can warp a sequence of letters to fit a shape while preserving their readability