Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed production and extended for further investigation. However, various factors like loosely organized codebases sophisticated model configurations complicate easy reuse important innovations a wide audience. Though there on-going eff...

In document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image ...

Document Image Binarization is performed in the preprocessing stage for document analysis and it aims to segment the foreground text from the document background. A fast and accurate document image binarization technique is important for the ensuing document image processing tasks such as optical character recognition (OCR) and Document Image Retrieval (DIR). This research area has been studied...

Document image analysis is the study of converting documents from paper form to an electronic form that captures the information content of the document. Necessary processing includes recognition of document layout (to determine reading order, and to distinguish text from diagrams), recognition of text (called Optical Character Recognition, OCR), and processing of diagrams and photographs. The ...

This paper presents a new model based document image segmentation scheme that uses XML-DTDs (eXtensible Mark-up Language-Document Type Definition). Given a document image, the algorithm has the ability to select the appropriate model. A new wavelet based tool has been designed for distinguishing text from non-text regions and characterization of font sizes. Our model based analysis scheme makes...

Hyperspectral imaging and analysis refers to the capture and understanding of image content in multiple spectral channels. Satellite and airborne hyperspectral imaging has been the focus of research in remote sensing applications since nearly the past three decades. Recent use of ground-based hyperspectral imaging has found immense interest in areas such as medical imaging, art and archaeology,...

Both users and developers of OCR systems benefit from objective performance evaluation and benchmarking. The need for improved tests has given additional impetus to research on evaluation methodology. We discuss some of the statistical and combinatorial principles underlying error estimation, propose a taxonomy for reference data, and review evaluation paradigms in current use. We provide point...

We resort to preference learning in order to address the problem of acquiring necessary knowledge in two distinct steps of the document image analysis process: 1) reading order detection, and 2) document summarization. We advocate a relational approach for both cases and we propose a probabilistic relational learning method. Experiments on real data for both applications prove the effectiveness...

One of the main tasks of digital image analysis is to recognize the properties of real objects based on their digital images. These images are obtained by some sampling device, like a CCD camera, and are represented as finite sets of points that are assigned some value in a gray level or color scale. A fundamental question in image understanding is which features in the digital image correspond...