Segmentation of text from badly degraded document images is a very challenging task due
to the high inter/intra-variation between the document background and the foreground text of …

The increasing availability of high-performance, low-priced, portable digital imaging devices
has created a tremendous opportunity for supplementing traditional scanning for document …

The increasing availability of high performance, low priced, portable digital imaging devices
has created a tremendous opportunity for supplementing traditional scanning for document …

We consider the problem of document binarization as a pre-processing step for optical
character recognition (OCR) for the purpose of keyword search of historical printed …

In this paper, we propose a technique called LACT for automatically categorizing software
systems in open-source repositories. LACT is based on latent Dirichlet Allocation, an …

In this paper, we address the problem of the identification of text in noisy document images.
We are especially focused on segmenting and identifying between handwriting and …

Computational perception has indeed been dramatically modified and reformed from
handcrafted feature-based techniques to the advent of deep learning. Scene text …

Inspired by the success of MRF models for solving object segmentation problems, we
formulate the binarization problem in this framework. We represent the pixels in a document …

Thinning algorithms often cause stroke distortions at the crosses or intersections of strokes,
which lead to bad results in pattern recognition tasks. In order to overcome these drawbacks …

While modern off-the-shelf OCR engines show particularly high accuracy on scanned text,
text detection and recognition in natural images still remains a challenging problem. Here …