Name
Abstract | ||
|---|---|---|
Due to the fact that historical handwritten documents present many degradations, pre-processing of such documents is considered as a big challenge. Most pre-processing methods and specifically binarization return better results when they are applied on printed documents. We present in this paper a binarization approach adaptive for handwritten historical documents based on extraction of regions-of-interest. During our tests several images datasets are used, the benchmarking datasets for binarization DIBCO 2009 and H-DIBCO 2010 (15 images) as well as complete handwritten documents from the IAM historical database (about 60 images). The evaluation of the proposed binarization method is based on several evaluation metrics for binarization. The results show that the proposed method fit with handwritten historical documents (FM about 88%) for images of the binarization competitions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICFHR.2012.261 | Frontiers in Handwriting Recognition |
Keywords | Field | DocType |
complete handwritten document,images datasets,binarization dibco,binarization approach adaptive,binarization competition,proposed binarization method,historical handwritten document,local binarization approach,iam historical database,benchmarking datasets,handwritten ancient documents,handwritten historical document,history,feature extraction,image recognition,image segmentation | Pattern recognition,Computer science,Document image processing,Image segmentation,Feature extraction,Artificial intelligence,Benchmarking | Conference |
ISSN | ISBN | Citations |
2167-6445 | 978-1-4673-2262-1 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ines Ben Messaoud | 1 | 59 | 6.58 |
| Hamid Amiri | 2 | 86 | 19.36 |
| Haikal El-Abed | 3 | 436 | 29.39 |
| Volker Margner | 4 | 107 | 6.37 |