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| Author | L. Rothacker; Marçal Rusiñol; G.A. Fink | ||||
| Title | Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1305 - 1309 | ||
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| Abstract | Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset. | ||||
| Address | Washington; USA; August 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
Call Number  |
Admin @ si @ RRF2013 | Serial | 2344 | ||
| Permanent link to this record | |||||
| Author | M. Visani; V.C.Kieu; Alicia Fornes; N.Journet | ||||
| Title | The ICDAR 2013 Music Scores Competition: Staff Removal | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1439-1443 | ||
| Keywords | |||||
| Abstract | The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results. | ||||
| Address | Washington; USA; August 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.045; 600.061 | Approved | no | ||
| Call Number |
Admin @ si @ VKF2013 | Serial | 2338 | ||
| Permanent link to this record | |||||