  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.

Computer Vision and Pattern Recognition