Feature Extraction Methods for Character Recognition

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Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

An investigation into the use of genetic algorithms for shape recognition

The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated

A Cognitive Approach to the Translation of Creative Metaphor in Othello and Macbeth from English into Arabic

ABSTRACT Despite the intriguing nature of metaphor and its acknowledged importance in the discipline of Translation Studies (TS), a relatively small number of studies have explored the translation of metaphor from the perspective of Conceptual Metaphor Theory, and very few of them adopted an experiential approach to the object of analysis. This research aims at exploring the translatability of creative metaphor in six Arabic translations of Shakespeare’s Othello and Macbeth based on a combined methodology that adopts the Conceptual Theory of Metaphor and the descriptive approach to text analysis in TS. The empirical study argues that metaphor translatability is an experiential process that is highly influenced by the diversity and richness of our conceptual system and the background knowledge shared by the metaphor producer and metaphor translator. Discussing metaphor translatability from the perspective of these factors involves dealing with different levels of variation in our metaphoric thinking including the cultural, contextual and pragmatic levels. The analyses and discussions of the empirical study mark a departure from text-linguistic approaches to the topic in that they deal with the Source Text’s and Target Text’s metaphoric content as physically embedded conceptual models rather than linguistic patterns with grammatically delineated features and structures. The arguments of the study answer several questions with regard to researching the translation of metaphor from the perspective of Conceptual Theory, providing a detailed description of what exactly influences the process and product of translation, and underlining the functionality of the variation factor in appreciating the conceptual nature of metaphor. The results of the empirical research reveal that, although our metaphoric thinking has a universally shared metaphoric structure, not all our metaphors are translatable or translated in a single way, which refutes the supremacy of the notion of metaphor universality, putting emphasis on the factors of experientialism, exposure and intentionality

Arbitrary Keyword Spotting in Handwritten Documents

Despite the existence of electronic media in today’s world, a considerable amount of written communications is in paper form such as books, bank cheques, contracts, etc. There is an increasing demand for the automation of information extraction, classification, search, and retrieval of documents. The goal of this research is to develop a complete methodology for the spotting of arbitrary keywords in handwritten document images. We propose a top-down approach to the spotting of keywords in document images. Our approach is composed of two major steps: segmentation and decision. In the former, we generate the word hypotheses. In the latter, we decide whether a generated word hypothesis is a specific keyword or not. We carry out the decision step through a two-level classification where first, we assign an input image to a keyword or non-keyword class; and then transcribe the image if it is passed as a keyword. By reducing the problem from the image domain to the text domain, we do not only address the search problem in handwritten documents, but also the classification and retrieval, without the need for the transcription of the whole document image. The main contribution of this thesis is the development of a generalized minimum edit distance for handwritten words, and to prove that this distance is equivalent to an Ergodic Hidden Markov Model (EHMM). To the best of our knowledge, this work is the first to present an exact 2D model for the temporal information in handwriting while satisfying practical constraints. Some other contributions of this research include: 1) removal of page margins based on corner detection in projection profiles; 2) removal of noise patterns in handwritten images using expectation maximization and fuzzy inference systems; 3) extraction of text lines based on fast Fourier-based steerable filtering; 4) segmentation of characters based on skeletal graphs; and 5) merging of broken characters based on graph partitioning. Our experiments with a benchmark database of handwritten English documents and a real-world collection of handwritten French documents indicate that, even without any word/document-level training, our results are comparable with two state-of-the-art word spotting systems for English and French documents

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p