This paper analyzes the problems of document image recognition and existing solutions. Document algorithms have been studied for quite a long time, but despite this, currently, topic is relevant research continues, as evidenced by large number associated publications reviews. However, most these works reviews are devoted to individual tasks. In this review, entire set methods, approaches, neces...

Abstract This paper presents a systematic literature review of image datasets for document analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate analysis is crucial prerequisite to facilitate research using different machine learning algorithms. However, because the very large variety actual data (e.g., scripts, tasks, dates, support s...

We report an investigation into strategies, algorithms, and software tools for document image content extraction and inventory, that is, the location and measurement of regions containing handwriting, machine-printed text, photographs, blank space, etc. We have developed automatically trainable methods, adaptable to many kinds of documents represented as bilevel, greylevel, or color images, tha...

We report in this paper a robust implementation of document segmentation and classification. In this algorithm, top-down approach is adopted due to its fast processing speed and reliability. In addition, the approach accounts for every portion of the processed page and it is also capable of image display a t different scale and allowing editing of classified blocks such as changing, merging and...

land cover information is one of the most important prerequisite in urban management system. in this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. considering the complicated and dense urban areas in third world countries, object based approaches are suggested as an effective image processing technique. the purpose of this paper are the introduc...

We present a method for unsupervised content based classification of images. The idea is to first segment the images using centroid models common to all the images in the set and then through bringing an analogy between models/images and words/documents to apply algorithms from the field of unsupervised document classification to cluster the images. We choose our centroid models to be histogram...