APPLICATION OF GUARDED FUZZY-ATTRIBUTE CONTEXT FREE GRAMMAR TO SYNTACTIC PATTERN RECOGNITION

1992 ◽  
Vol 06 (05) ◽  
pp. 777-797 ◽  
Author(s):  
KWOK-PING CHAN
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Concurrent Programming ◽  
High Dimensional ◽  
Simple Scheme ◽  
Simple Modification ◽  
Context Free Grammar ◽  
Syntactic Pattern ◽  
On Line ◽  
Context Free

High-dimensional grammars such as web grammars and plex grammars were used in syntactic recognition of complex 2-D or 3-D objects. In this paper, we present a simple modification, borrowing the concept of guards from concurrent programming to attributed grammar proposed by D. E. Knuth. We show that the resultant grammar can handle patterns described by the high-dimensional grammars. The only problem is that we may not have a simple ordering of the terminal symbols or pattern primitives. In some applications, such as on-line character recognition, the problem does not exist and hence presents a good candidate for the application. We also discuss the incorporation of fuzzy attributes and the necessary modification is hence introduced. Finally, the error transformations proposed by K. S. Fu can easily be taken into consideration and a powerful yet simple scheme presented.

scholarly journals A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

2022 ◽  
Vol 16 ◽  
pp. 23-27
Author(s):  
Mehrnoosh Bazrafkan
Keyword(s):  
Pattern Recognition ◽  
Data Structure ◽  
Time Complexity ◽  
Theoretic Approach ◽  
Top Down ◽  
Syntactic Pattern Recognition ◽  
Context Free Grammar ◽  
Linked List ◽  
Syntactic Pattern ◽  
Context Free

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

AN IMPROVED STRUCTURAL APPROACH FOR AUTOMATED RECOGNITION OF HANDPRINTED CHARACTERS

1991 ◽  
Vol 05 (01n02) ◽  
pp. 97-121 ◽  
Author(s):  
PATRICK SHEN-PEI WANG ◽  
AMAR GUPTA
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Character Recognition ◽  
Line Drawing ◽  
Lower Degree ◽  
Automated Recognition ◽  
Chain Code ◽  
Pattern Recognition Methods ◽  
On Line ◽  
Descriptive Language

This paper examines several line-drawing pattern recognition methods for handwritten character recognition. They are the picture descriptive language (PDL), Berthod and Maroy (BM), extended Freeman's chain code (EFC), error transformation (ET), tree grammar (TG), and array grammar (AG) methods. A new character recognition scheme that uses improved extended octal codes as primitives is introduced. This scheme offers the advantages of handling flexible sizes, orientations, and variations, the need for fewer learning samples, and lower degree of ambiguity. Finally, the simulation of off-line character recognition by the real-time on-line counterpart is investigated.

GROWTH TRANSFORMATIONS FOR PROBABILISTIC FUNCTIONS OF STOCHASTIC GRAMMARS

1996 ◽  
Vol 10 (03) ◽  
pp. 183-201 ◽  
Author(s):  
FRANCISCO CASACUBERTA
Keyword(s):  
Pattern Recognition ◽  
Continuous Extension ◽  
Syntactic Pattern Recognition ◽  
Estimation Algorithms ◽  
Syntactic Pattern ◽  
Training Samples ◽  
Finite Set ◽  
Stochastic Grammars ◽  
Context Free ◽  
Context Free Grammars

Stochastic Grammars are the most usual models in Syntactic Pattern Recognition. Both components of a Stochastic Grammar, the characteristic grammar and the probabilities attached to the rules, can be learnt automatically from training samples. In this paper, first a review of some algorithms are presented to infer the probabilistic component of Stochastic Regular and Context-Free Grammars under the framework of the Growth Transformations. On the other hand, with Stochastic Grammars, the patterns must be represented as strings over a finite set of symbols. However, the most natural representation in many Syntactic Pattern Recognition applications (i.e. speech) is as sequences of vectors from a feature vector space, that is, a continuous representation. Therefore, to obtain a discrete representation of the patterns, some quantization errors are introduced in the representation process. To avoid this drawback, a formal presentation of a semi-continuous extension of the Stochastic Regular and Context-Free Grammars is studied and probabilistic estimation algorithms are developed in this paper. In this extension, sequences of vectors, instead of strings of symbols, can be processed with Stochastic Grammars.

APPLICATIONS OF NEURAL NETWORKS TO CHARACTER RECOGNITION

1991 ◽  
Vol 05 (01n02) ◽  
pp. 353-382 ◽  
Author(s):  
I. GUYON
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Expert Knowledge ◽  
Learning Algorithms ◽  
Network Architectures ◽  
General Introduction ◽  
On Line ◽  
The Many

Among the many applications that have been proposed for neural networks, character recognition has been one of the most successful. Compared to other methods used in pattern recognition, the advantage of neural networks is that they offer a lot of flexibility to the designer, i.e. expert knowledge can be introduced into the architecture to reduce the number of parameters determined by training by examples. In this paper, a general introduction to neural network architectures and learning algorithms commonly used for pattern recognition problems is given. The design of a neural network character recognizer for on-line recognition of handwritten characters is then described in detail.

PARALLEL RECOGNITION OF HIGH DIMENSIONAL IMAGES

1992 ◽  
Vol 06 (02n03) ◽  
pp. 285-291 ◽  
Author(s):  
M. NIVAT ◽  
A. SAOUDI
Keyword(s):  
Time Complexity ◽  
Random Access ◽  
Space Complexity ◽  
Recognition Problem ◽  
High Dimensional ◽  
Context Free Grammar ◽  
Random Access Machine ◽  
Parallel Random Access Machine ◽  
Context Free ◽  
Context Free Grammars

We investigate the complexity of the recognition of images generated by a class of context-free image grammars. We show that the sequential time complexity of the recognition of an n × n image as generated by a context-free grammar is O(nM(n)), where M(n) is the time to multiply two boolean n × n matrices. The space complexity of this recognition is O(n3). Using a parallel random access machine (i.e. PRAM), the recognition can be done in O( log 2(n)) time with n7 processors or in O(n log 2(n)) time with n6 processors. We also introduce high dimensional context-free grammars and prove that their recognition problem is polylogarithmic.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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