Bruno Tenório Ávila

A fast orientation and skew detection algorithm for monochromatic document images

Very often in the digitization process, documents are either not placed with the correct orientation or are rotated of small angles in relation to the original image axis. These factors make more difficult the visualization of images by human users, increase the complexity of any sort of automatic image recognition, degrade the performance of OCR tools, increase the space needed for image storage, etc. This paper presents a fast algorithm for orientation and skew detection for complex monochromatic document images, which is capable of detecting any document rotation at a high precision.

A new algorithm for removing noisy borders from monochromatic documents

This paper presents a new and efficient algorithm for removing noisy borders in monochromatic images of documents introduced by the digitalisation process using automatically fed scanners.

A New Algorithm for Skew Detection in Images of Documents

Very frequently the digitalisation process of documents produce images rotated of small angles in relation to the original image axis. The skew introduced makes more difficult the visualisation of images by human users. Besides that, it increases the complexity of any sort of automatic image recognition, degrades the performance of OCR tools, increases the space needed for image storage, etc. Thus, skew correction is an important part of any document processing system being a matter of concern of researchers for almost two decades now. The search for faster and good quality solutions to this problem is still on. This paper presents an efficient algorithm for skew detection and correction of images of documents including non-textual graphical elements, such as pictures and tables. The new algorithm was tested in over 10,000 images yielding satisfactory performance.

BigBatch – an environment for processing monochromatic documents

BigBatch is a processing environment designed to automatically process batches of millions of monochromatic images of documents generated by production line scanners. It removes noisy borders, checks and corrects orientation, calculates and compensates the skew angle, crops the image standardizing document sizes, and finally compresses it according to user defined file format. BigBatch encompasses the best and recently developed algorithms for such kind of document images. BigBatch may work either in standalone or operator assisted modes. Besides that, BigBatch in standalone mode is able to process in clusters of workstations or in grids.

A new rotation algorithm for monochromatic images

The classical rotation algorithm applied to monochromatic images introduces white holes in black areas, making edges uneven and disconnecting neighboring elements. Several algorithms in the literature address only the white hole problem. This paper proposes a new algorithm that solves those three problems, producing better quality images.

A New Sentence Similarity Method Based on a Three-Layer Sentence Representation

Sentence similarity methods are used to assess the degree of likelihood between phrases. Many natural language applications such as text summarization, information retrieval, text categorization, and machine translation employ measures of sentence similarity. The existing approaches for this problem represent sentences as vectors of bag of words or the syntactic information of the words in the phrase. The likelihood between phrases is calculated by composing the similarity between the words in the sentences. Such schemes do not address two important concerns in the area, however: the semantic problem and the word order. This paper proposes a new sentence similarity measure that attempts to address such problems by taking into account the lexical, syntactic, and semantic analysis of sentences. The new similarity measure proposed outperforms the state of the art systems in around 6%, when tested using a standard and publically available dataset.

A platform for language independent summarization

The text data available on the Internet is not only huge in volume, but also in diversity of subject, quality and idiom. Such factors make it infeasible to efficiently scavenge useful information from it. Automatic text summarization is a possible solution for efficiently addressing such a problem, because it aims to sieve the relevant information in documents by creating shorter versions of the text. However, most of the techniques and tools available for automatic text summarization are designed only for the English language, which is a severe restriction. There are multilingual platforms that support, at most, 2 languages. This paper proposes a language independent summarization platform that provides corpus acquisition, language classification, translation and text summarization for 25 different languages.

W-tree: A Compact External Memory Representation for Webgraphs

World Wide Web applications need to use, constantly update, and maintain large webgraphs for executing several tasks, such as calculating the web impact factor, finding hubs and authorities, performing link analysis by webometrics tools, and ranking webpages by web search engines. Such webgraphs need to use a large amount of main memory, and, frequently, they do not completely fit in, even if compressed. Therefore, applications require the use of external memory. This article presents a new compact representation for webgraphs, called w-tree, which is designed specifically for external memory. It supports the execution of basic queries (e.g., full read, random read, and batch random read), set-oriented queries (e.g., superset, subset, equality, overlap, range, inlink, and co-inlink), and some advanced queries, such as edge reciprocal and hub and authority. Furthermore, a new layout tree designed specifically for webgraphs is also proposed, reducing the overall storage cost and allowing the random read query to be performed with an asymptotically faster runtime in the worst case. To validate the advantages of the w-tree, a series of experiments are performed to assess an implementation of the w-tree comparing it to a compact main memory representation. The results obtained show that w-tree is competitive in compression time and rate and in query time, which may execute several orders of magnitude faster for set-oriented queries than its competitors. The results provide empirical evidence that it is feasible to use a compact external memory representation for webgraphs in real applications, contradicting the previous assumptions made by several researchers.

Meta-Fibonacci Codes:Efficient Universal Coding of Natural Numbers

In this paper, we address the problem of the universal coding of natural numbers. A new numeration system is introduced, which is based on variable-

Using Function Points in Agile Projects: A Comparative Analysis Between Existing Approaches

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