Name
Abstract | ||
|---|---|---|
In this paper we present a fully trainable binarization solution for degraded document images. Unlike previous attempts that often used simple features with a series of pre- and post-processing, our solution encodes all heuristics about whether or not a pixel is foreground text into a high-dimensional feature vector and learns a more complicated decision function. In particular, we prepare features of three types: 1) existing features for binarization such as intensity [1], contrast [2], [3], and Laplacian [4], [5]; 2) reformulated features from existing binarization decision functions such those in [6] and [7]; and 3) our newly developed features, namely the Logarithm Intensity Percentile (LIP) and the Relative Darkness Index (RDI). Our initial experimental results show that using only selected samples (about 1.5% of all available training data), we can achieve a binarization performance comparable to those fine-tuned (typically by hand), state-of-the-art methods. Additionally, the trained document binarization classifier shows good generalization capabilities on out-of-domain data. |
Year | Venue | Field |
|---|---|---|
2015 | ICIP | Computer science,Heuristics,Artificial intelligence,Logarithm,Classifier (linguistics),Training set,Computer vision,Feature vector,Pattern recognition,Pixel,Simple Features,Machine learning,Laplace operator |
DocType | Volume | Citations |
Journal | abs/1505.00529 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Wu | 1 | 331 | 31.69 |
| stephen rawls | 2 | 59 | 4.08 |
| Wael Abd-Almageed | 3 | 248 | 24.52 |
| Premkumar Natarajan | 4 | 874 | 79.46 |