Deep neural networks has shown great success in computer vision fields by achieving considerable state-of-the-art results and are beginning to arouse big interest in the document analysis community. In this paper, we present a novel siamese deep network of three inputs that allows retrieving the most similar words to a given query. The proposed system follows a query-by-example approach according to a segmentation-based technique and aims to learn suitable representations of handwritten word images, for which a simple Euclidean distance could perform the matching. The results obtained for the George Washington dataset show the potential and the effectiveness of the proposed keyword spotting system.

It is the pleasure of the organizing committee to welcome all participants to the 2010 IAPR Workshop on Document Analysis Systems (DAS). This year's workshop is being held June 9-11th in Boston, Massachusetts, a location situated in the heart of beautiful New England in the northeastern United States. Boston has a rich history that dates back to the 1600's and was a major focal point in the history of the American Revolution and America's fight for independence. Over the years, Boston has developed into a center for industrial, academic and cultural excellence and draws millions of visitors each year. DAS 2010 is the ninth workshop in a series. The first DAS was held in Kaiserslautern, Germany in 1994, and was followed by Malvern, PA (1996); Nagano, Japan (1998); Rio de Janeiro, Brazil (2000); Princeton, NJ (2002); Florence, Italy (2004); Nelson, New Zealand (2006) and Nara, Japan (2008). The DAS tradition is to bring together industry, academic and government researchers interested in many aspects of document analysis systems and to provide opportunities for fruitful interaction and collaboration. This year's workshop is organized as a three-day, single track event, with oral and poster presentations, as well as working group discussions on the second and third afternoons. Special sessions on contributed datasets and a keynote talk on this same topic provide a compelling theme, and we hope this focus will help push the field toward greater sharing of data and accepted standards for evaluation. This year, we received 91 submissions from 25 countries on six continents. The papers were reviewed by 45 members of our research community and an international program committee representing 16 different countries. The overall quality was excellent and we have chosen 28 full papers for oral presentation and 37 as poster papers, as well as 15 short papers that will be presented either as posters or in a special short oral format. In addition, six groups are scheduled to present live demos during the poster sessions. Full papers underwent the standard peer review process and will appear in the official workshop proceedings to be published in the ACM International Conference Proceedings Series, available online as part of the ACM Digital Library. The short papers are included in the unofficial hardcopy proceedings distributed at the event as well as on the DAS 2010 website.

It is the pleasure of the organizing committee to welcome all participants to the 2010 IAPR Workshop on Document Analysis Systems (DAS). This year's workshop is being held June 9-11th in Boston, Massachusetts, a location situated in the heart of beautiful New England in the northeastern United States. Boston has a rich history that dates back to the 1600's and was a major focal point in the history of the American Revolution and America's fight for independence. Over the years, Boston has developed into a center for industrial, academic and cultural excellence and draws millions of visitors each year. DAS 2010 is the ninth workshop in a series. The first DAS was held in Kaiserslautern, Germany in 1994, and was followed by Malvern, PA (1996); Nagano, Japan (1998); Rio de Janeiro, Brazil (2000); Princeton, NJ (2002); Florence, Italy (2004); Nelson, New Zealand (2006) and Nara, Japan (2008). The DAS tradition is to bring together industry, academic and government researchers interested in many aspects of document analysis systems and to provide opportunities for fruitful interaction and collaboration. This year's workshop is organized as a three-day, single track event, with oral and poster presentations, as well as working group discussions on the second and third afternoons. Special sessions on contributed datasets and a keynote talk on this same topic provide a compelling theme, and we hope this focus will help push the field toward greater sharing of data and accepted standards for evaluation. This year, we received 91 submissions from 25 countries on six continents. The papers were reviewed by 45 members of our research community and an international program committee representing 16 different countries. The overall quality was excellent and we have chosen 28 full papers for oral presentation and 37 as poster papers, as well as 15 short papers that will be presented either as posters or in a special short oral format. In addition, six groups are scheduled to present live demos during the poster sessions. Full papers underwent the standard peer review process and will appear in the official workshop proceedings to be published in the ACM International Conference Proceedings Series, available online as part of the ACM Digital Library. The short papers are included in the unofficial hardcopy proceedings distributed at the event as well as on the DAS 2010 website.

Writer recognition is considered as a difficult problem to solve due to variations found in the writing, even from the same writer. In this paper, Steered Hermite Features are used to identify writer from a written document. We will show that Steered Hermite Features are highly useful for text images because they extract lot of information, no- tably for data characterized by oriented features, curves and segments. The algorithm we propose here, first calcu- lates the Steered Hermite Features of the images which are then passed on to Support Vector Machine for training and testing. The base of tests consists of sample of some lines of writings (five at most) of primarily diversified writings of authors from IAM database. With the proposed algorithm based on Steered Hermite Features, we were able to achieve an accuracy of around 83% percent for a set of 30 authors with non overlapping images of written text.

This paper presents a new use of the curvelet transform as a multiscale method for indexing linear singularities and curved handwritten shapes in documents images. As it belongs to the wavelet family, this representation can be useful at several scales of details. The proposed scheme for handwritten shape characterization targets to detect oriented and curved fragments at different scales so as to compose an unique signature for each handwritten analyzed samples. In this way, curvelets coefficients are used as a representation tool for handwriting when searching in large manuscripts databases by finding similar handwritten samples. Current results of ancient manuscripts retrieval are very promising with very satisfying precisions and recalls.

In this paper, we propose a global segmentation free methodology for patrimonial documents denoising, handwriting characterization and categorization that are based on biological inspired approaches. We widely used here the spectral domain of handwritten images by frequency decompositions: Hermite transforms and Gabor bank filters. Handwritten pages are described by a multiscale signature that is based on orientation features and that is at the basis of a similarity measure. The current results of handwriting categorization and indexing are very promising and show that it is possible to analyze handwritten drawings without any a priori graphemes segmentation.

In this paper we propose to define a measure of visualsimilarity to compare different pages in a corpus. Thismeasure is based on the analysis of the visual layoutsaliency of the page composition. This similarity iscomputed using both the document layout andcharacteristics of the text itself. The text characterizationuses statistical features derived from textural primitives.Our purpose is to establish perceptive links betweendocuments in order to facilitate their storage and theirretrieval. In this paper we present two possibleapplications of this measure of similarity: the query ofthe corpus by example and the documents classification.In the first application, we extract documents that are themost visually similar to a document, given as query. Inthe second application, the similarity measure is used toclassify the document under investigation using its visualsimilarity to a reference set of documents. Our test corpusis extracted from the Finland MTDB Oulu multi-genredatabase that provides a great diversity of page layoutsand contents.