Document images can be affected by many degradation scenarios, which cause recognition
and processing difficulties. In this age of digitization, it is important to denoise them for …

Document image enhancement methods are often used to improve the accuracy and
efficiency of automated document analysis and recognition tasks such as character …

Artificial neural networks have been shown significant performance in various image-to-
image conversion tasks. However, complex conversions often require a large number of …

Document image binarization is an important pre-processing step in document analysis and
archiving. The state-of-the-art models for document image binarization are variants of …

This paper analyzes the problems of document image recognition and the existing solutions.
Document recognition algorithms have been studied for quite a long time, but despite this …

Binarization plays an important role in document analysis and recognition (DAR) systems. In
this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten …

Automatic optical recognition of documents is a traditional function of modern document
processing systems. In this context, recognition represents a complex process which …

Binarization of ancient documents is a challenging task. Nowadays lot of traditional
binarization algorithms exist with good accuracy but those algorithms cannot remove all kind …

Quantized low-precision neural networks are very popular because they require less
computational resources for inference and can provide high performance, which is vital for …

One of the most computationally intensive parts in modern recognition systems is an
inference of deep neural networks that are used for image classification, segmentation …