Michael Heaney
580 California St., Suite 400
San Francisco, CA, 94104
Content-based image recognition on printed broadside ballads: The Bodleian Libraries' ImageMatch Tool
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Abstract
offers some discussion on the benefits of ImageMatch's for image-cataloguing in Rare Books collections.
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Second International Conference on Document Image Analysis for Libraries (DIAL'06), 2006
This paper lies on the field of ancient patrimonial books valorization: it precisely relates to the development of suitable assistance tools for humanists and historians to help them to retrieve information in great corpus of digitized documents. This paper presents a part of this ambitious project and deals with the presentation of a pixel classification method for ancient typewritten documents. The presented approach lies on a multiresolution maps construction and analysis. For 5 resolutions we construct 5 different characterisation maps. All the maps are based on texture information (correlation of pixels orientations, grey level pixel density…). After the merging of these 25 maps, each pixel of the original image is described by a vector which allows the computing of a hierarchical classification. In order to avoid issues linked to the binarization process, all or maps are computed on grey level images. The system has been tested on a CESR database of ancient printed books of the Renaissance. The classification results are evaluated through several visual classification illustrations.
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Antique printed books constitute a heritage that should be preserved and used. With novel digitising techniques is now possible to have these books stored in digital format and accessible to a wider public. However it remains the problem of how to use them. DEBORA (Digital accEss to BOoks of the RenAissance) is a European project that aims to develop a system to interact with these books through worldwide networks. The main issue is to build a database accessible through client computers. That will require to built accompanying metadata that should characterise different components of the books as illuminated letters, banners, figures and key words in order to simplify and speed up the remote access. To solve these problems, digital image analysis algorithms regarding filtering, segmentation, separation of text from non-text, lines and word segmentation and word recognition were developed. Some novel ideas are presented and illustrated through examples.
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While historically digital heritage libraries were first powered in image mode, they quickly took advantage of OCR technology to index printed collections and consequently improve the scope and performance of the information retrieval services offered to users. But the access to iconographic resources has not progressed in the same way, and the latter remain in the shadows: manual incomplete and heterogeneous indexation, data silos by iconographic genre. Today, however, it would be possible to make better use of these resources, especially by exploiting the enormous volumes of OCR produced during the last two decades, and thus valorize these engravings, drawings, photographs, maps, etc. for their own value but also as an attractive entry point into the collections, supporting discovery and serenpidity from document to document and collection to collection. This article presents an ETL (extract-transform-load) approach to this need, that aims to: Identify and extract iconography wher...
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In this article, we propose a method of characterization of pictures of old documents based on a texture approach. This characterization is carried out with the help of a multiresolution study of the textures contained in the pictures of the document. So, by extracting five features linked to the frequencies and to the orientations in the different parts of a page, it is possible to extract and to compare elements of high semantic level without expressing any hypothesis about the physical or logical structure of the analysed documents. Experiments show the feasibility of the fulfillment of tools for the navigation or the indexation help. In these experimentations, we will lay the emphasis upon the pertinence of these texture features and the advances that they represent in terms of characterization of content of a deeply heterogeneous corpus.
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The Indexing and Retrieval of Document Images: A Survey
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Image Collation: Matching illustrations in manuscripts
https://arxiv.org/abs/2108.08109, 2021
Illustrations are an essential transmission instrument. For an historian, the first step in studying their evolution in a corpus of similar manuscripts is to identify which ones correspond to each other. This image collation task is daunting for manuscripts separated by many lost copies, spreading over centuries, which might have been completely reorganized and greatly modified to adapt to novel knowledge or belief and include hundreds of illustrations. Our contributions in this paper are threefold. First, we introduce the task of illustration collation and a large annotated public dataset to evaluate solutions, including 6 manuscripts of 2 different texts with more than 2 000 illustrations and 1 200 annotated correspondences. Second, we analyze state of the art similarity measures for this task and show that they succeed in simple cases but struggle for large manuscripts when the illustrations have undergone very significant changes and are discriminated only by fine details. Finally, we show clear evidence that significant performance boosts can be expected by exploiting cycle-consistent correspondences. Our code and data are available on http://imagine.enpc.fr/~shenx/ImageCollation.
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A Pixel Labeling Framework for Comparing Texture Features: Application to Digitized Ancient Books
In this article, a complete framework for the comparative analysis of texture features is presented and evaluated for the segmentation and characterization of ancient book pages. Firstly, the content of an entire book is characterized by extracting the texture attributes of each page. The extraction of the texture features is based on a multiresolution analysis. Secondly, a clustering approach is performed in order to classify automatically the homogeneous regions of book pages. Namely, two approaches are compared based on two different statistical categories of texture features, autocorrelation and co-occurrence, in order to segment the content of ancient book pages and find homogeneous regions with little a priori knowledge. By computing several clustering and classification accuracy measures, the results of the comparison show the effectiveness of the proposed framework. Tests on different book contents (text vs. graphics, manuscript vs. printed) show that those texture features are more suitable to distinguish textual regions from graphical ones, than to distinguish text fonts.
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Books printed from wooden blocks in the fifteenth century, the so-called xylographa or blockbooks, are exceedingly rare and therefore extremely valuable objects of the European cultural heritage. The paper gives an introduction into the material and the questions examined in current research. Responding to scholars' needs for high-quality reproductions of the blockbooks and their watermarks, a project for the cataloguing and digitisation of the c. 90 blockbooks held at the Bayerische Staatsbibliothek München and other institutions in Bavaria was started in 2009. The aims and methods of the project are described in the paper, and the workflow of the digitisation project carried out at the Bayerische Staatsbibliothek is explained in detail, both with respect to the digitisation procedures for image capture and the detection and documentation of watermarks with an infrared camera. The illustrations exemplify the quality of digital images generated in the project, all of which are accessible on the website of the Munich Digiti-Zation Center (MDZ) at the Bayerische Staatsbibliothek München.
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Automatic Feature Extraction on Pages of Antique Books Through a Mathematical Morphology Based Methodology
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This paper presents a mathematical morphology based methodology to identify and extract several components on antique printed books in order to automatically build metadata. These components were previously classified i nto five different sets (drop capitals, stripes, figures, annotations and text matter) each one characterised by particular geometric features. Based on that assumption several novel algorithms appealing to morphological operators are proposed. The evaluation of the methodology is performed on pages of XVI century books.
A Structural Signature Based on Texture for Digitized Historical Book Page Categorization
The work conducted in this article presents a structural signature based on texture for the characterization and categorization of digitized historical book pages. The proposed signature does not assume a priori knowledge regarding page layout and content, and hence, it is applicable to a large variety of ancient books. By integrating varying low-level features (e.g. texture) characterizing the different page components (i.e. different text fonts or graphic regions) on the one hand, and structural information describing the page layout on the other hand, the proposed signature provides a rich and holistic description of the layout and content of the analyzed book pages. More precisely, the signature-based characterization approach consists of two stages. The first stage is extracting automatically homogeneous regions. Then, the second one is proposing a graph-based page signature, which is based on the extracted homogeneous regions, reflecting its layout and content. This signature ensures the implementation of numerous applications for managing effectively a corpus or collections of books (e.g. information retrieval in digital libraries according to several criteria or page categorization). To illustrate the effectiveness of the proposed page signature, a detailed experimental evaluation has been conducted in this article for assessing two possible categorization applications, unsupervised page classification and page stream segmentation.
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