Fourier descriptors and handwritten digit recognition

This paper presents the results of a comparative study of various Fourier descriptor representations and their use in recognition of unconstrained handwritten digits. Certain characteristics of five distinct Fourier descriptor representations of handwritten digits are discussed, and illustrations of ambiguous digit classes introduced by use of these Fourier descriptor representations are presented. It is concluded that Fourier descriptors are practically effective only within the framework of an intelligent system, capable of reasoning about digit hypotheses. We describe a hypothesisgenerating algorithm based on Fourier descriptors which allows a classifier to associate more than one digit class with each input. Such hypothesis-generating schemes can be very effective in systems employing multiple classifiers. We compare the performance of the five Fourier descriptor representations based on experiment results produced by a particular hypothesis-generating classifier for a test set of 14000 handwritten digits. It is found that some Fourier descriptor formulations are more successful than others for handwritten digit recognition.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46057/1/138_2005_Article_BF01212429.pd

Real-time hand printed character recognition on a DSP chip

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 119-120).by Christopher Isaac Chang.M.S

Recognition of off-line handwritten cursive text

The author presents novel algorithms to design unconstrained handwriting recognition systems organized in three parts: In Part One, novel algorithms are presented for processing of Arabic text prior to recognition. Algorithms are described to convert a thinned image of a stroke to a straight line approximation. Novel heuristic algorithms and novel theorems are presented to determine start and end vertices of an off-line image of a stroke. A straight line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. The resulting ordering of the stroke segments is a suitable preprocessed representation for subsequent handwriting recognition algorithms as it helps to segment the stroke. The algorithm was tested against one data set of isolated handwritten characters and another data set of cursive handwriting, each provided by 20 subjects, and has been 91.9% and 91.8% successful for these two data sets, respectively. In Part Two, an entirely novel fuzzy set-sequential machine character recognition system is presented. Fuzzy sequential machines are defined to work as recognizers of handwritten strokes. An algorithm to obtain a deterministic fuzzy sequential machine from a stroke representation, that is capable of recognizing that stroke and its variants, is presented. An algorithm is developed to merge two fuzzy machines into one machine. The learning algorithm is a combination of many described algorithms. The system was tested against isolated handwritten characters provided by 20 subjects resulting in 95.8% recognition rate which is encouraging and shows that the system is highly flexible in dealing with shape and size variations. In Part Three, also an entirely novel text recognition system, capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. This system is an extension of the above recognition system. Tokens are extracted from a onedimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. It is shown how to obtain a deterministic fuzzy sequential machine from a token representation that is capable'of recognizing that token and its variants. An algorithm for token learning is presented. The tokens of a stroke are re-combined to meaningful strings of tokens. Algorithms to recognize and learn token strings are described. The. recognition stage uses algorithms of the learning stage. The process of extracting the best set of basic shapes which represent the best set of token strings that constitute an unknown stroke is described. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4% and 51.1%, respectively

Studies of inspection algorithms and associated microprogrammable hardware implementations

This work is concerned with the design and development of real-time algorithms for industrial inspection applications. Rather than implement algorithms in dedicated hardware, microprogrammable machines were considered essential in order to maintain flexibility. After a survey of image pattern recognition where algorithms applicable to real-time use are cited, this thesis presents industrial inspection algorithms that locate and scrutinise actual manufactured products. These are fast and robust - a necessary requirement in industrial environments. The National Physical Laboratory have developed a Linear Array Processor (LAP) specifically designed for industrial recognition work. As with most array processors, the LAP has a greater performance than conventional processors, yet is strictly limited to parallel algorithms for optimum performance. It was therefore necessary to incorporate sequentialism into the design of a multiprocessor system. A microcoded bit-slice Sequential Image Processor (SIP) has been designed and built at RHBNC in conjunction with the NPL. This was primarily intended as a post-processor for the LAP based on the VMEbus but in fact has proved its usefulness as a stand-alone processor. This is described along with an assembler written for SIP which translates assembly language mnemonics to microcode. This work, which includes a review of current architectures, leads to the specification of a hybrid (SIMD/NIMD) architecture consisting of multiple autonomous sequential processors. This involves an analysis of various configurations and entails an investigation of the source of bottlenecks within each design. Such systems require a significant amount of interprocessor communication: methods for achieving this are discussed, some of which have only become practical with the decrease incost of electronic components. This eventually leads to a system for which algorithm execution speed increases approximately linearly with the number of processors. The algorithms described in earlier chapters are examined on the system and the practicalities of such a design are analysed in detail. Overall, this thesis has arrived at designs of programmable real-time inspection systems, and has obtained guidelines which will help with the implementation of future inspection systems.<p