Name
Abstract | ||
|---|---|---|
In previous work we showed that shape descriptor features can be used in Look Up Table (LUT) classifiers to learn patterns of degradation and correction in historical document images. The algorithm encodes the pixel neighborhood information effectively using a variant of shape descriptor. However, the generation of the shape descriptor features was approached in a heuristic manner. In this work, we propose a system of learning the shape features from the training data set by using neural networks: Multilayer Perceptrons (MLP) for feature extraction. Given that the MLP maybe restricted by a limited dataset, we apply a feature selection algorithm to generalize, and thus improve, the feature set obtained from the MLP. We validate the effectiveness and efficiency of the proposed approach via experimental results. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.838746 | DOCUMENT RECOGNITION AND RETRIEVAL XVII |
Keywords | Field | DocType |
image enhancement, historical documents, machine learning, artificial neural networks, document image analysis | Heuristic,Feature selection,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Pixel,Document retrieval,Artificial neural network,Perceptron,Shape analysis (digital geometry) | Conference |
Volume | ISSN | Citations |
7534 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tayo Obafemi-Ajayi | 1 | 25 | 4.83 |
| Gady Agam | 2 | 391 | 43.99 |
| Ophir Frieder | 3 | 3300 | 419.55 |