Image processing applications using a novel parallel computing machine based on reconfigurable logic

Zelig is a 32 physical node fine-grained computer employing field-programmable gate arrays. Its application to the high speed implementation of various image pre-processing operations (in particular binary morphology) is described together with typical speed-up result

Historical Handwritten Text Images Word Spotting through Sliding Window HOG Features

In this paper we present an innovative technique to semi-automatically index handwritten word images. The proposed method is based on HOG descriptors and exploits Dynamic Time Warping technique to compare feature vectors elaborated from single handwritten words. Our strategy is applied to a new challenging dataset extracted from Italian civil registries of the XIX century. Experimental results, compared with some previously developed word spotting strategies, confirmed that our method outperforms competitors

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Graph-based keyword spotting in historical handwritten documents

The amount of handwritten documents that is digitally available is rapidly increasing. However, we observe a certain lack of accessibility to these documents especially with respect to searching and browsing. This paper aims at closing this gap by means of a novel method for keyword spotting in ancient handwritten documents. The proposed system relies on a keypoint-based graph representation for individual words. Keypoints are characteristic points in a word image that are represented by nodes, while edges are employed to represent strokes between two keypoints. The basic task of keyword spotting is then conducted by a recent approximation algorithm for graph edit distance. The novel framework for graph-based keyword spotting is tested on the George Washington dataset on which a state-of-the-art reference system is clearly outperformed.Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). S+SSPR 2016: Structural, Syntactic, and Statistical Pattern Recognition pp. 564-573.http://link.springer.combookseries/5582017-11-05hj2017Informatic

General memory neural network-extending the properties of basis networks to RAM-based architectures

A common framework for architectures combining multiple vector-quantization of the input space with memory look-up operations is proposed. Properties of the model are discussed and, in particular, a close relationship with basis functions networks (such as RBFs and kernel regression networks) is establishe

Application of the CMAC input encoding scheme in the N-tuple approximation network

The N-tuple approximation network offers many advantages over conventional neural networks in terms of speed of operation and its ability to realise arbitrary nonlinear mappings. However, its generalisation/selectivity properties depend strongly on the form of input encoding being used in the system. The paper analyses the suitability of use of the CMAC code for the N-tuple networks, and compares its properties with existing schemes. It is argued that the application of this type of encoding can provide desirable monotonic mapping between input and pattern space distances without the penalty of very long binary patterns as is the case for bar-chart encoding. Additionally, similarities between the classic N-tuple and CMAC networks are highlighte

Euclidean input mapping in a N-tuple approximation network

A type of the N-tuple neural architecture can be shown to perform function approximation based on local interpolation, similar that performed by RBF networks. Since the size and speed of operation in this implementation are independent of the training set size, it is attractive for practical adaptive solutions. However, the kernel function used by the network is non-Euclidean, which can cause performance losses for high-dimensional input data. The authors investigate methods for realising more isotropic kernel basis functions by use of special data encoding technique

A principled approach to N-tuple recognition systems

The idea of n-tuple sampling as a basis for pattern recognition, as proposed by Bledsoe and Browning (1959), remains a viable approach to a range of pattern classification tasks especially where speed of learning is of importance. The formal relationship between n-tuple neural networks and more mainstream network paradigms, such as radial basis function networks, and classical nonparametric pattern classifiers, such as kernel estimation, is considered, and it is described how the classic n-tuple recogniser and the n-tuple regression network form differing approximations in the classification proces