117,625 research outputs found
ImUnipen image data set for writer identification (N=208) - vectorial handwriting converted to usable images
No full text
==============
Terms of Usage
==============
The ImUnipen data set is intended for non-commercial, scientific use,
and is distributed under auspices of the Unipen Foundation.
Please always refer to the following paper in IEEE PAMI when using
the ImUnipen data set:
Bulacu, M.; Schomaker, L.
Text-Independent Writer Identification and Verification
Using Textural and Allographic Features
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 29, Issue 4, April 2007 Page(s):701 - 717
The ImUnipen data set is derived from the Unipen (unipen.org)
data set of on-line (i.e., vectorial, xy) handwriting.
The xy-coordinates and a line-generator algorithm are used
to generate a raster image, as if the data were optically scanned.
Contents: for 208 writers, there are two PNG images per writer of
an artificially constructed table of naturally written words (49MByte).
These words are pasted onto a white page. For systematics reasons,
we call such a page a Paragraph, see below.
The file names are organized as (example):
Writ990221.Doc01.Par00.png
Writ990221.Doc01.Par01.png
meaning: writer number 990221, document 01 (there exists only Doc01)
and the image with artificial "paragraph" of isolated words "Par00"
and "Par01".
The Par00 and Pa01 images are typically used as the query
and best match in a leave-one-out setting for writer identification.
For instance, Par00 is the query, and Par01 is added to the total set
of all other images as the attractor for an identification search.
For these experiments, word labels are not given in this data set,
on purpose, as the goal is to test recognition-free writer identification
methods.
For a description of the regular
Unipen data set, please visit http://unipen.org
Lambert Schomaker constructed this set in 2005</p
Adaptive Recognition of Online, Cursive Handwriting
No full textIn earlier studies, a stroke-oriented recognizer (VHS) of on-line cursive handwriting is reported [Thomassen et al., 1988; Schomaker & Teulings, 1990; Teulings et al., 1990; Schomaker & Teulings, 1992; Schomaker, 1993]. This system uses a movement-theoretical segmentation into strokes as the starting point of the recognition process. The pen-tip trajectory of a written word is low-pass filtered, and geometrically normalized with respect to size and slant. The absolute velocity of the pen-tip displacement is calculated, and the signal is segmented in strokes, each stroke being the trajectory between two robust minima in the absolute velocity [Teulings et al., 1987]. Strokes are characterized by feature vectors that are clustered using a Kohonen Self-Organizing Map as a feature quantizer. In the current system, as opposed to earlier versions, a number of typical problems in connected-cursive and mixed-cursive script recognition are dealt with, such as t-bar crossing,..
ImUnipen image data set for writer identification (N=208) - vectorial handwriting converted to usable images
No full textThe ImUnipen data set is intended for non-commercial, scientific use, and is distributed under auspices of the Unipen Foundation. Please always refer to the following paper in IEEE PAMI when using the ImUnipen data set: Bulacu, M.; Schomaker, L. Text-Independent Writer Identification and Verification Using Textural and Allographic Features Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 29, Issue 4, April 2007 Page(s):701 - 717 The ImUnipen data set is derived from the Unipen (unipen.org) data set of on-line (i.e., vectorial, xy) handwriting. The xy-coordinates and a line-generator algorithm are used to generate a raster image, as if the data were optically scanned. Contents: for 208 writers, there are two PNG images per writer of an artificially constructed table of naturally written words (49MByte). These words are pasted onto a white page. For systematics reasons, we call such a page a Paragraph, see below. The file names are organized as (example): Writ990221.Doc01.Par00.png Writ990221.Doc01.Par01.png meaning: writer number 990221, document 01 (there exists only Doc01) and the image with artificial "paragraph" of isolated words "Par00" and "Par01". The Par00 and Pa01 images are typically used as the query and best match in a leave-one-out setting for writer identification. For instance, Par00 is the query, and Par01 is added to the total set of all other images as the attractor for an identification search. For these experiments, word labels are not given in this data set, on purpose, as the goal is to test recognition-free writer identification methods. For a description of the regular Unipen data set, please visit http://unipen.org Lambert Schomaker constructed this set in 200
Patoka River NWR Vegetation Cover Spatial Database (2001)
No full textThe University of Minnesota, Department of Forest Resources has created a high resolution
land cover spatial database of Patoka River National Wildlife Refuge (NWR),
in Gibson and Pike Counties, Indiana. U.S. Fish and Wildlife Service
(USFWS) 1:15000, color- infrared aerial photos were collected on October 3, 2001.Research supported by Cooperative Agreement USDI/1434-HQ-97-RU-01566 WO 49 between the
University of Minnesota and the U.S. Fish and Wildlife Service.Sieracki, Jennifer L.; Burk, Thomas E.; Schomaker, John H.. (2002). Patoka River NWR Vegetation Cover Spatial Database (2001). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/37480
The Monk Line Segmentation (MLS) Dataset
No full text<p>Overview</p>
<p>The MLS dataset available from this page consists of 31 handwritten page scans. The dataset contains medieval, historical and contemporary manuscripts, and has the purpose of testing line-segmentation algorithms. The collection contains a wide variation of the common problems in handwriting recognition: lines with overlapping ascenders/descenders, slightly rotated scans and curved base lines. <br>
</p>
<p>Download</p>
<p>The MLS dataset was collected from the Monk system as of Friday May 17 14:15:04 CEST 2013. It was collected by Lambert Schomaker in May 2013 at the Institution of Artificial Intelligence and Cognitive Engineering (ALICE), University of Gronigen. </p>
<p>The tar.gz file contains the image dataset for historical manuscripts. For more details please refer to the README file in the tar.gz file. The dataset downloaded for research use only. © 2013 Copyright. <br>
</p>
<p>@INPROCEEDINGS{Surinta:2014:ICFHR,<br>
author = {O. Surinta and M. Holtkamp and M. F. Karaaba and JP. van Oosten and L. R. B. Schomaker and M. A. Wiering},<br>
title = {A* Path Planning for Line Segmentation of Handwritten Documents},<br>
booktitle = {Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},<br>
year = {2014},<br>
month = {Sep},<br>
pages = {175-180},<br>
numpages = {6},<br>
isbn = {978-1-4799-4335-7},<br>
issn = {2167-6445},<br>
publisher = {IEEE},<br>
doi = {http://dx.doi.org/10.1109/ICFHR.2014.37},<br>
}</p>
Rare plant monitoring Lakeview BLM District, 2018
No full text"During the spring and summer of 2018, we monitored 18 plant species (15 vascular, three non-vascular) listed as special status species (SSS) in the Bureau of Land Management's (BLM) Lakeview Resource Area. Approximately 2,000 acres were surveyed and 12 of the 18 target species were observed over the survey period stretching from April 11th to June 26th"--Executive summary.report to the Bureau of Land Management, Lakeview District ; report prepared by Meaghan I. Petix, Matt A. Bahm, A. Lisa Schomaker, and Denise E.L. Giles.Title from PDF caption (viewed on February 4, 2022).This archived document is maintained by the State Library of Oregon. It is for informational purposes and may not be suitable for legal purposes.Includes bibliographical references (page 12).Mode of access: Internet from the State Library of Oregon U.S. Government Publications Collection.Text in English
Is incremental semantic interpretation related to end-of-sentence verification?: Evidence from correlation analyses
No full textKnoeferle P, Urbach TP, Kutas M. Is incremental semantic interpretation related to end-of-sentence verification?: Evidence from correlation analyses. In: Taatgen N, Rijn van H, Schomaker L, Nerbonne J, eds. Proceedings of the Annual Conference of the Cognitive Science Society. Cognitive Science Society, Inc; 2009: 1127-1132
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
