Name
Abstract | ||
|---|---|---|
This paper presents an empirical study on the application of ranking fusion methods in the context of handwriting information retrieval. Several works in the electronic text-domain suggest that significant improvements in retrieval performance can be achieved by combining different approaches to IR. In the handwritten-domain, two quite different families of retrieval approaches are encountered. The first family is based on standard approaches carried out on texts obtained through handwriting recognition, therefore regarded as noisy texts, while the second one is recognition-free using word spotting algorithms. Given the large differences that exist between these two families of approaches (document and query representations, matching methods, etc.), we hypothesize that fusion methods applied to the handwritten-domain can also bring significant effectiveness improvements. Results show that for texts having a word error rate (wer) lower than 23%, the performances achieved with the combined system are close to the performances obtained with clean digital texts, i.e. without transcription errors. In addition, for poorly recognized texts (wer 52%), improvements can also be obtained with standard fusion methods. Furthermore, we present a detailed analysis of the fusion performances, and show that existing indicators of expected improvements are not accurate in our context. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12275-0_24 | ECIR |
Keywords | Field | DocType |
on-line handwriting information retrieval,retrieval approach,standard fusion method,fusion performance,different family,retrieval performance,ranking fusion method,handwriting recognition,different approach,fusion method,handwriting information retrieval,information retrieval,word error rate,empirical study | Relevance feedback,Handwriting,Computer science,Handwriting recognition,Artificial intelligence,Natural language processing,Spotting,Empirical research,Information retrieval,Ranking,Word error rate,Sensor fusion,Machine learning | Conference |
Volume | ISSN | ISBN |
5993 | 0302-9743 | 3-642-12274-4 |
Citations | PageRank | References |
2 | 0.37 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastián Peña Saldarriaga | 1 | 15 | 4.06 |
| Emmanuel Morin | 2 | 42 | 16.13 |
| Christian Viard-Gaudin | 3 | 444 | 46.20 |