As a main part of several document analysis systems, Skew estimation represents one of the major research defies, particularly in case of historical documents exploration. In this paper, we propose an original skew angle detection and correction technique. Morphological Skeleton is introduced to considerably diminish the amount of data by eliminating the redundant pixels and preserving only the central curves of the image components. Next, the proposed method uses Progressive Probabilistic Hough Transform (PPHT) to find image lines. At the end, a specific procedure is applied in order to measure the global skew angle of the document image from these identified lines. Experimental results demonstrate the accuracy and the effectiveness of our approach on skew angle detection upon three popular datasets covering many types of documents of diverse linguistic writings (Chinese, Greek and English) and different styles (horizontal or vertical orientations, including figures and tables, multi-columns page layouts).
Purpose – The join operations between data streams need more time and request more energy than traditional joins. In wireless sensor networks, energy is a critical factor. The survival of the network depends on this energy, thus it is necessary to consider, for this type of queries in such networks, the reduction of the sensors’ energy consumption. While works that have been done to treat n-way join operations between data streams are rare so far, we propose a technique, named NSLSJ (N-way Stream Local Semi-Join) to perform this type of join operations. The principal aim is to considerably reduce the consumed energy.
Methodology/approach/design – The technique 'N-way Stream Local Semi-Join (NSLSJ) proposed in this paper is based on an in-network execution, and on filtering tuples strategy for an important gain in energy.
Findings – Compared to NSLJ and Sens-Join techniques, NSLSJ shows better performances in the realized tests as it consumes less energy.
Abstract:In wireless sensors networks, data are sensed and recorded as databases, and then acceded by relational queries. Joins are queries that are largely used. Joins collect data from several nodes’ table. These are operations that typically consume a lot of energy because they generate a large number of messages in the network. Researchers worked to decrease this consumed energy. Many strategies were proposed in this way, but most of them addressed only binary joins. N-way joins received few interests. N-way joins perform join operations between more than two tables. They cause greater energy consumption. Additionally, the number of execution order is very important; it grows exponentially with the number of considered tables.
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