This document proposes a binary graphics recognition algorithm based on fitting functions. It involves fitting line segments from graphics with polynomial functions, and comparing the fitting functions to templates in a knowledge base to identify the graphics. The algorithm represents graphics as sets of line segments, feature points, and best fitting vectors. It then uses the analysis of variance of fitting functions to recognize graphics by finding the most similar template function.
● Concept of Structure and Union
● Declaration and Initialization of structure and union
● Nested structures
● Array of Structures
● Passing structure to functions
● Introduction to Arrays
● Declaration and initialization of one dimensional and two-dimensional
arrays.
● Definition and initialization of String
● String functions
This document discusses numerical concepts in C++ including NaN, IND, INF, and DEN. It provides:
- Definitions and examples of each concept
- Non-standard and standard representations of each in memory
- How each appears when displayed or printed
- Properties like comparison and calculations involving each value
- Notes that the representations shown are not definitive and the IEEE standard specifies floating point formats
The document is intended to share knowledge of how these special numerical values are handled in C++ for those dealing with floating point arithmetic and extensive calculations.
C++ provides several fundamental and derived data types to store and represent different types of data. Fundamental data types include integers, characters, floats, doubles, and void. Integers can be further classified as short, int, long and modified with signed or unsigned. Characters can also be signed or unsigned. Floats and doubles represent real numbers with different levels of precision. Derived data types include arrays to store multiple values of the same type, structures to group different data types, unions to access memory in different types, enumerations to define named integer constants, classes to define user-defined types with methods and pointers to reference memory locations.
C# is an object-oriented programming language designed for building a wide range of applications that run on the .NET Framework. The document provides examples of simple C# programs that demonstrate key elements like namespaces, classes, methods, variables, operators, and arrays. It also summarizes the different data types in C# including integers, floats, decimals, Booleans, strings, and arrays. Code samples are included to illustrate how to declare and use variables of different types, write simple programs, and perform operations with arrays.
The document discusses input and output functions in C language like getchar, putchar, scanf, printf, gets and puts. It explains that these functions allow transfer of data between computer and standard input/output devices. Specifically, it describes the getchar and putchar functions for single character input/output, and the scanf and printf functions for transferring various data types including integers, floats and strings. It also briefly explains the gets and puts functions for string input/output.
(4) cpp automatic arrays_pointers_c-stringsNico Ludwig
Check out these exercises: http://de.slideshare.net/nicolayludwig/4-cpp-automatic-arrayspointerscstringsexercises-38510502
- Pointers: Call by Value versus Call by Reference
- Automatic Arrays
- Arrays and Pointer Decay
- Pointers to Pointers
- C-strings as Arrays and their Memory Representation
- Basic C-string Functions
Embedded C is a variant of C programming language used for embedded systems. It uses a cross-compiler to convert programs into machine code for the target processor. A cross-compiler runs on one system but compiles code for another system. Pointers in embedded C store the address of a variable in memory and can be used to access the value at that address using dereference operator (*). Arrays, functions, loops and other C constructs are used similarly in embedded C to structure programs and access hardware.
● Concept of Structure and Union
● Declaration and Initialization of structure and union
● Nested structures
● Array of Structures
● Passing structure to functions
● Introduction to Arrays
● Declaration and initialization of one dimensional and two-dimensional
arrays.
● Definition and initialization of String
● String functions
This document discusses numerical concepts in C++ including NaN, IND, INF, and DEN. It provides:
- Definitions and examples of each concept
- Non-standard and standard representations of each in memory
- How each appears when displayed or printed
- Properties like comparison and calculations involving each value
- Notes that the representations shown are not definitive and the IEEE standard specifies floating point formats
The document is intended to share knowledge of how these special numerical values are handled in C++ for those dealing with floating point arithmetic and extensive calculations.
C++ provides several fundamental and derived data types to store and represent different types of data. Fundamental data types include integers, characters, floats, doubles, and void. Integers can be further classified as short, int, long and modified with signed or unsigned. Characters can also be signed or unsigned. Floats and doubles represent real numbers with different levels of precision. Derived data types include arrays to store multiple values of the same type, structures to group different data types, unions to access memory in different types, enumerations to define named integer constants, classes to define user-defined types with methods and pointers to reference memory locations.
C# is an object-oriented programming language designed for building a wide range of applications that run on the .NET Framework. The document provides examples of simple C# programs that demonstrate key elements like namespaces, classes, methods, variables, operators, and arrays. It also summarizes the different data types in C# including integers, floats, decimals, Booleans, strings, and arrays. Code samples are included to illustrate how to declare and use variables of different types, write simple programs, and perform operations with arrays.
The document discusses input and output functions in C language like getchar, putchar, scanf, printf, gets and puts. It explains that these functions allow transfer of data between computer and standard input/output devices. Specifically, it describes the getchar and putchar functions for single character input/output, and the scanf and printf functions for transferring various data types including integers, floats and strings. It also briefly explains the gets and puts functions for string input/output.
(4) cpp automatic arrays_pointers_c-stringsNico Ludwig
Check out these exercises: http://de.slideshare.net/nicolayludwig/4-cpp-automatic-arrayspointerscstringsexercises-38510502
- Pointers: Call by Value versus Call by Reference
- Automatic Arrays
- Arrays and Pointer Decay
- Pointers to Pointers
- C-strings as Arrays and their Memory Representation
- Basic C-string Functions
Embedded C is a variant of C programming language used for embedded systems. It uses a cross-compiler to convert programs into machine code for the target processor. A cross-compiler runs on one system but compiles code for another system. Pointers in embedded C store the address of a variable in memory and can be used to access the value at that address using dereference operator (*). Arrays, functions, loops and other C constructs are used similarly in embedded C to structure programs and access hardware.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document describes a method for recognizing Marathi handwritten numerals using support vector machines. Feature extraction is performed using Fourier descriptors and normalized chain codes extracted from the numeral contours. A dataset of 12690 Marathi numeral samples was used, achieving a recognition accuracy of 98.15% using five-fold cross validation with support vector machine classification. Key steps included preprocessing, contour extraction, feature extraction using Fourier descriptors and normalized chain codes, and classification using support vector machines.
The document discusses primitive data types in C#, including integer, floating-point, boolean, character, and string types. It defines each type, provides examples of declaring variables of each type and assigning values, and describes literals that can be used to represent values of different types. Key points covered include the name, size, and default value of each primitive type as well as demonstrations of declaring and initializing variables in C#.
This presentation covers C++ data types, variables, constants, and input/output. It discusses the different fundamental data types in C++ like int, char, float, and bool. It also explains variables, which name memory locations, constants, which are values that don't change, and input/output streams like cout and cin. The goal is for students to understand these basic C++ concepts and be able to write simple programs.
This document discusses applying a generic programming approach to string management in C++. It finds that STL-based sequences like std::vector and std::basic_string, combined with STL algorithms, provide a simple and effective way to represent and manipulate strings. Common string operations like searching, modifying, and converting between types can be implemented independently of the underlying string representation using function templates and iterators. This approach is more flexible than solutions tied to a single string type.
This document discusses abstract classes in object-oriented programming. It begins with an example of operators with similar getPrecedence() methods that could be made more concise using an abstract class. It then discusses how abstract classes allow for polymorphic code that works generically for subclasses by defining abstract methods that must be implemented by concrete subclasses. The document uses examples of drawing shapes to illustrate abstract classes and polymorphism. It concludes by applying these concepts to create an abstract Op class for the operator examples to make their code more concise and generic.
This document summarizes research investigating the properties of graphs produced by discrete exponentiation. It presents theoretical predictions for statistics of random binary functional graphs, such as the number of components and cyclic nodes. These predictions are tested by generating discrete exponentiation graphs modulo different primes and comparing the observed statistics, finding errors within 1%.
Lukas Renggli is a software engineer at Google who specializes in meta-models. He discusses several meta-models he has worked with including Protocol Buffers for serialization, Magritte for generic services, Google Web Toolkit for model-driven web development, and Helvetia for programming languages. He explains how each meta-model provides practical benefits like extensibility, language independence, and enabling the creation of specialized tools and applications.
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...Marko Rodriguez
A graph is a data structure that links a set of vertices by a set of edges. Modern graph databases support multi-relational graph structures, where there exist different types of vertices (e.g. people, places, items) and different types of edges (e.g. friend, lives at, purchased). By means of index-free adjacency, graph databases are optimized for graph traversals and are interacted with through a graph traversal engine. A graph traversal is defined as an abstract path whose instance is realized on a graph dataset. Graph databases and traversals can be used for searching, scoring, ranking, and in concert, recommendation. This presentation will explore graph structures, algorithms, traversal algebras, graph-related software suites, and a host of examples demonstrating how to solve real-world problems, in real-time, with graphs. This is a whirlwind tour of the theory and application of graphs.
How To Code in C# The Complete Course. From data types to object orientation. Includes code samples and exercises.
Topics
Getting Started with C#
C# Language Fundamentals
Branching
Operators
Object-Orientated Programming
Classes and Objects
Inside Methods
Debugging
Inheritance and Polymorphism
Operator Overloading
Structs
Interfaces
Arrays
Collection Interfaces and Types
Strings
Throwing and Catching Exceptions
Delegates and EventsGenerics
New Language Features
RSA ALGORITHM WITH A NEW APPROACH ENCRYPTION AND DECRYPTION MESSAGE TEXT BY A...ijcisjournal
In many research works, there has been an orientation to studying and developing many of the applications of public-key cryptography to secure the data while transmitting in the systems, In this paper we present an approach to encrypt and decrypt the message text according to the ASCII(American Standard Code for Information Interchange) and RSA algorithm by converting the message text into binary representation and dividing this representation to bytes(8s of 0s and 1s) and applying a bijective function between the group of those bytes and the group of characters of ASCII and then using this mechanism to be compatible with using RSA algorithm, finally, Java application was built to apply this approach directly.
Extending Boyer-Moore Algorithm to an Abstract String Matching ProblemLiwei Ren任力偉
The bad character shift rule of Boyer-Moore string search algorithm is studied in this paper for the purpose of extending it to more general string match problems. An abstract problem of string match is defined in general. An optimized string match algorithm based one the bad character heuristics is proposed to solve the abstract match problem efficiently.
The document provides an introduction to a Java programming course. It outlines the course objectives which include understanding core Java concepts like primitive data types, control flow, methods, arrays, object-oriented programming, and core Java classes. It also discusses how upon completing the course students will be able to develop programs using Eclipse IDE and write simple programs using various Java features. The document then covers specific topics that will be taught like methods, object-oriented programming concepts like classes, constructors, and polymorphism.
The document discusses copyright, trademarks, fair use, and piracy. Copyright grants exclusive rights over published works. Trademarks uniquely identify businesses and must be registered. Fair use allows limited copying without permission in some cases. Piracy involves illegally downloading or distributing copyrighted media and can result in expensive fines.
This document showcases a variety of coastal-themed merchandise including mugs, plaques, trays, platters, buckets, and photo frames featuring crabs, mermaids, seashells, and more. The items range in price from $2.75 to $14 with suggested retail prices being approximately double the list prices. A beach mini tag program offering 5 of each style for $82.50 is also highlighted.
The document is a collection of 63 predictions for content marketing in 2014 from 24 different experts. It discusses trends like content marketing continuing to grow and become integrated into overall marketing plans, the importance of high-quality and personalized content, and the need for content promotion and distribution. Many predictions focus on specific content formats and strategies, such as video, newsrooms, visual storytelling, and global/local approaches, that will become more prevalent. Overall it suggests that in 2014, content marketing will move beyond a buzzword and quality will become more important than quantity for many brands.
2014 Social Media Forecasts From The ExpertsHeidi Cohen
Want to know where social media is headed in 2014? Here are 67 social media forecasts from 27 experts to get your social media strategy and plans on track.
If you enjoy this presentation, please read the full text of these predictions on Heidi Cohen’s Actionable Marketing Guide [http://HeidiCohen.com/?p=21440 ] & share it with your colleagues.
This document provides 35 Excel tips to help save time when working with spreadsheets. It covers topics like splitting windows, hiding and unhiding rows/columns, using functions like SUM, IF, VLOOKUP and HLOOKUP, sorting data, conditional formatting, and more advanced tools like Solver, data tables, scenarios and pivot tables. For each tip it provides a brief explanation of how and why to use the feature and includes an example exercise.
Work from height event 26.09.13 chester le streetAlan Bassett
Working from Height in Construction - which is a half day event (choose morning or afternoon session) to be held on Thursday, 26th September 2013 at Durham County Cricket Club, The Riverside, Chester le Street.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document describes a method for recognizing Marathi handwritten numerals using support vector machines. Feature extraction is performed using Fourier descriptors and normalized chain codes extracted from the numeral contours. A dataset of 12690 Marathi numeral samples was used, achieving a recognition accuracy of 98.15% using five-fold cross validation with support vector machine classification. Key steps included preprocessing, contour extraction, feature extraction using Fourier descriptors and normalized chain codes, and classification using support vector machines.
The document discusses primitive data types in C#, including integer, floating-point, boolean, character, and string types. It defines each type, provides examples of declaring variables of each type and assigning values, and describes literals that can be used to represent values of different types. Key points covered include the name, size, and default value of each primitive type as well as demonstrations of declaring and initializing variables in C#.
This presentation covers C++ data types, variables, constants, and input/output. It discusses the different fundamental data types in C++ like int, char, float, and bool. It also explains variables, which name memory locations, constants, which are values that don't change, and input/output streams like cout and cin. The goal is for students to understand these basic C++ concepts and be able to write simple programs.
This document discusses applying a generic programming approach to string management in C++. It finds that STL-based sequences like std::vector and std::basic_string, combined with STL algorithms, provide a simple and effective way to represent and manipulate strings. Common string operations like searching, modifying, and converting between types can be implemented independently of the underlying string representation using function templates and iterators. This approach is more flexible than solutions tied to a single string type.
This document discusses abstract classes in object-oriented programming. It begins with an example of operators with similar getPrecedence() methods that could be made more concise using an abstract class. It then discusses how abstract classes allow for polymorphic code that works generically for subclasses by defining abstract methods that must be implemented by concrete subclasses. The document uses examples of drawing shapes to illustrate abstract classes and polymorphism. It concludes by applying these concepts to create an abstract Op class for the operator examples to make their code more concise and generic.
This document summarizes research investigating the properties of graphs produced by discrete exponentiation. It presents theoretical predictions for statistics of random binary functional graphs, such as the number of components and cyclic nodes. These predictions are tested by generating discrete exponentiation graphs modulo different primes and comparing the observed statistics, finding errors within 1%.
Lukas Renggli is a software engineer at Google who specializes in meta-models. He discusses several meta-models he has worked with including Protocol Buffers for serialization, Magritte for generic services, Google Web Toolkit for model-driven web development, and Helvetia for programming languages. He explains how each meta-model provides practical benefits like extensibility, language independence, and enabling the creation of specialized tools and applications.
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...Marko Rodriguez
A graph is a data structure that links a set of vertices by a set of edges. Modern graph databases support multi-relational graph structures, where there exist different types of vertices (e.g. people, places, items) and different types of edges (e.g. friend, lives at, purchased). By means of index-free adjacency, graph databases are optimized for graph traversals and are interacted with through a graph traversal engine. A graph traversal is defined as an abstract path whose instance is realized on a graph dataset. Graph databases and traversals can be used for searching, scoring, ranking, and in concert, recommendation. This presentation will explore graph structures, algorithms, traversal algebras, graph-related software suites, and a host of examples demonstrating how to solve real-world problems, in real-time, with graphs. This is a whirlwind tour of the theory and application of graphs.
How To Code in C# The Complete Course. From data types to object orientation. Includes code samples and exercises.
Topics
Getting Started with C#
C# Language Fundamentals
Branching
Operators
Object-Orientated Programming
Classes and Objects
Inside Methods
Debugging
Inheritance and Polymorphism
Operator Overloading
Structs
Interfaces
Arrays
Collection Interfaces and Types
Strings
Throwing and Catching Exceptions
Delegates and EventsGenerics
New Language Features
RSA ALGORITHM WITH A NEW APPROACH ENCRYPTION AND DECRYPTION MESSAGE TEXT BY A...ijcisjournal
In many research works, there has been an orientation to studying and developing many of the applications of public-key cryptography to secure the data while transmitting in the systems, In this paper we present an approach to encrypt and decrypt the message text according to the ASCII(American Standard Code for Information Interchange) and RSA algorithm by converting the message text into binary representation and dividing this representation to bytes(8s of 0s and 1s) and applying a bijective function between the group of those bytes and the group of characters of ASCII and then using this mechanism to be compatible with using RSA algorithm, finally, Java application was built to apply this approach directly.
Extending Boyer-Moore Algorithm to an Abstract String Matching ProblemLiwei Ren任力偉
The bad character shift rule of Boyer-Moore string search algorithm is studied in this paper for the purpose of extending it to more general string match problems. An abstract problem of string match is defined in general. An optimized string match algorithm based one the bad character heuristics is proposed to solve the abstract match problem efficiently.
The document provides an introduction to a Java programming course. It outlines the course objectives which include understanding core Java concepts like primitive data types, control flow, methods, arrays, object-oriented programming, and core Java classes. It also discusses how upon completing the course students will be able to develop programs using Eclipse IDE and write simple programs using various Java features. The document then covers specific topics that will be taught like methods, object-oriented programming concepts like classes, constructors, and polymorphism.
The document discusses copyright, trademarks, fair use, and piracy. Copyright grants exclusive rights over published works. Trademarks uniquely identify businesses and must be registered. Fair use allows limited copying without permission in some cases. Piracy involves illegally downloading or distributing copyrighted media and can result in expensive fines.
This document showcases a variety of coastal-themed merchandise including mugs, plaques, trays, platters, buckets, and photo frames featuring crabs, mermaids, seashells, and more. The items range in price from $2.75 to $14 with suggested retail prices being approximately double the list prices. A beach mini tag program offering 5 of each style for $82.50 is also highlighted.
The document is a collection of 63 predictions for content marketing in 2014 from 24 different experts. It discusses trends like content marketing continuing to grow and become integrated into overall marketing plans, the importance of high-quality and personalized content, and the need for content promotion and distribution. Many predictions focus on specific content formats and strategies, such as video, newsrooms, visual storytelling, and global/local approaches, that will become more prevalent. Overall it suggests that in 2014, content marketing will move beyond a buzzword and quality will become more important than quantity for many brands.
2014 Social Media Forecasts From The ExpertsHeidi Cohen
Want to know where social media is headed in 2014? Here are 67 social media forecasts from 27 experts to get your social media strategy and plans on track.
If you enjoy this presentation, please read the full text of these predictions on Heidi Cohen’s Actionable Marketing Guide [http://HeidiCohen.com/?p=21440 ] & share it with your colleagues.
This document provides 35 Excel tips to help save time when working with spreadsheets. It covers topics like splitting windows, hiding and unhiding rows/columns, using functions like SUM, IF, VLOOKUP and HLOOKUP, sorting data, conditional formatting, and more advanced tools like Solver, data tables, scenarios and pivot tables. For each tip it provides a brief explanation of how and why to use the feature and includes an example exercise.
Work from height event 26.09.13 chester le streetAlan Bassett
Working from Height in Construction - which is a half day event (choose morning or afternoon session) to be held on Thursday, 26th September 2013 at Durham County Cricket Club, The Riverside, Chester le Street.
Smooth blends: How to add live online training to the e-learning mixBrightwave Group
http://www.brightwave.co.uk/livetime
What are the advantages of adding virtual classrooms to the e-learning mix? And, how you can develop your own successful blend?
Find out how in our practical guide. http://www.brightwave.co.uk/practical-guides/how-to-integrate-live-online-training-with-e-learning
The document is a series of phrases and keywords repeated multiple times that relate to business, career, and self-development topics such as marketing, leadership, challenges, growth, and change. The phrases are attributed to C. Grunewald of PushUp Marketing and appear to be ideas or themes for business consulting or training.
Floor Depot outlines their corporate social responsibility principles and initiatives related to environmental sustainability and social impact. Their 5R approach focuses on reducing pollution and deforestation by encouraging employees and customers to recycle, reuse, repair, resell, and replant. The goal is to simplify sustainability practices and encourage individuals to take CSR ownership through reducing waste and supporting reforestation from within.
An excellent toolbox talk to share with our colleagues courtesy of Cofely Fabricom.
This incident could have been easily avoided if the IP's hand was not in the line of fire. Make sure you utilise this excellent toolbox talk to prevent a similar injury which could have had the potential to be much worse.....
A road map to 2020 - how can learning technologies support fast-changing busi...Brightwave Group
These slides were originally presented as part of the Brightwave keynote debate at Learning Technologies
Brightwave's Question Time-style debate called 'A road map to 2020: How can learning technologies support fast-changing business practice?' last week cast new light on the future of learning technologies.
Networked Citizens a Cagliari: presentazione libro e open lesson Alessandro Lovari
Presentazione del libro "Networked Citizens. Comunicazione Pubblica e Amministrazioni Digitali", in occasione della giornata "Trasparenza, Partecipazione e Cittadini Connessi", organizzata il 5 aprile dalla Delegazione Sarda dell'Associazione della Comunicazione Pubblica e Istituzionale a Cagliari presso il FormezPA. Oltre alla presentazione le slide si riferiscono alla Open Lesson tenuta per i partecipanti e per gli iscritti on line.
This document provides guidance for people grieving the death of someone close to them who may have died by suicide. It discusses common feelings people experience in this situation, such as anger, depression, disbelief, guilt and despair. The guide aims to help bereaved individuals understand what they may be feeling and suggests seeking further support from others or support organizations. It also covers practical matters they may need to deal with after the death and offers encouragement that there is no right or wrong way to grieve. The overall message is that the reader is not alone in their experience and there are resources available to help them cope and heal.
American Reliance Group follows a five-fold financial planning process for its clients, who often share the common goal of living independently after retirement.
The documents discuss the Complete Streets movement, which advocates for roadways that are designed and operated to enable safe access and mobility for all users, including pedestrians, bicyclists, public transit riders, and motorists of all ages and abilities. Supporters argue that many streets currently prioritize automobiles and are unsafe for other modes of transportation. They note a growing interest among Americans in walking and biking more. Complete Streets policies require considering all road users in transportation planning and projects. Common features of Complete Streets include sidewalks, bike lanes, wide shoulders, and crosswalks.
Cavas Patagón is a grape producing and winemaking company located in Mendoza, Argentina. It has over 300 hectares of vineyards growing grapes varieties such as Malbec, Cabernet Sauvignon, Merlot, Syrah, Tempranillo, Chenin Blanc, Torrontes, Chardonnay, and Semillon. Cavas Patagón has partnered with wine cooperative San Carlos Sud to process its grapes and produce and bottle its wine brands at San Carlos Sud's wineries under the supervision of Cavas Patagón's enologist. This partnership provides Cavas Patagón with an annual production capacity of 14 million liters of premium red wine.
SCENE TEXT RECOGNITION IN MOBILE APPLICATION BY CHARACTER DESCRIPTOR AND STRU...Cheriyan K M
In text detection, our
previously proposed algorithms are applied to obtain text regions
from scene image. First, we design a discriminative character
descriptor by combining several state-of-the-art feature detectors
and descriptors. Second, we model character structure at each
character class by designing stroke configuration maps.
Text detection and recognition from natural sceneshemanthmcqueen
Text characters in natural scenes and surroundings provide us with valuable information about the place and even provide us with some legal/important information. Hence it’s very important for us to detect such text and recognise them which helps a lot. But , it’s not really easy to recognize those text information because of the diverse backgrounds and fonts used for the text. In this paper, a method is proposed to extract the text information from the surroundings. First, a character descriptor is designed with existing standard detectors and descriptors. Then, character structure is modeled at each character class by designing stroke configuration maps.In natural scenes , the text part is generally found on nearby sign boards and other objects. The extraction of such text is difficult because of noisy backgrounds and diverse fonts and text sizes. But many applications have been proven to be efficient in extraction of text from surroundings. For this , the method of text extraction is divided into two processes;
Text detection
Text recognition
The document discusses arrays and pointers in C programming. It defines arrays as homogeneous data types that allow a group of data items to be accessed using a single name and index. The document describes properties of arrays like type, length, and size. It also discusses initialization of one-dimensional and two-dimensional arrays. The document also covers strings as character arrays and functions in C including parameter passing, return values, and different types of functions.
ARABIC HANDWRITTEN CHARACTER RECOGNITION USING STRUCTURAL SHAPE DECOMPOSITIONsipij
This document summarizes a statistical framework for recognizing 2D shapes represented as arrangements of curves or strokes. It presents a hierarchical model with two layers: 1) At the lower level, each curve is represented by a point distribution model describing its shape variability. Curves are assigned stroke labels. 2) At the top level, shapes are represented by the geometric arrangement of curve center points (using another point distribution model) and a string of stroke labels. Recognition involves assigning stroke labels to curves, and recovering stroke and shape deformation parameters using expectation maximization. The method is applied to Arabic handwritten character recognition.
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...IRJET Journal
This document compares the K-SVD and WSQ algorithms for fingerprint compression. It provides an overview of both algorithms, including how they work, their advantages, and disadvantages. It also presents results of compressing different sized fingerprint images using each algorithm, showing that K-SVD consistently achieved smaller file sizes than WSQ. The document concludes that K-SVD is superior to WSQ for compressing fingerprint images.
HANDWRITTEN CHARACTER RECOGNITION USING STRUCTURAL SHAPE DECOMPOSITIONcsandit
This paper presents a statistical framework for recognising 2D shapes which are represented as
an arrangement of curves or strokes. The approach is a hierarchical one which mixes geometric
and symbolic information in a three-layer architecture. Each curve primitive is represented
using a point-distribution model which describes how its shape varies over a set of training
data. We assign stroke labels to the primitives and these indicate to which class they belong.
Shapes are decomposed into an arrangement of primitives and the global shape representation
has two components. The first of these is a second point distribution model that is used to
represent the geometric arrangement of the curve centre-points. The second component is a
string of stroke labels that represents the symbolic arrangement of strokes. Hence each shape
can be represented by a set of centre-point deformation parameters and a dictionary of
permissible stroke label configurations. The hierarchy is a two-level architecture in which the
curve models reside at the nonterminal lower level of the tree. The top level represents the curve
arrangements allowed by the dictionary of permissible stroke combinations. The aim in
recognition is to minimise the cross entropy between the probability distributions for geometric
alignment errors and curve label errors. We show how the stroke parameters, shape-alignment
parameters and stroke labels may be recovered by applying the expectation maximization EM
algorithm to the utility measure. We apply the resulting shape-recognition method to Arabic
character recognition.
An ANSI C Superset for Vector and Superscalar Computers and Its Retargetable ...Ann Wera
This document describes a C language superset called C[] that adds vector capabilities to allow writing efficient programs for SIMD (vector and superscalar) computer architectures. C[] adds notions of vectors as ordered sequences of values, allows arrays to have a step size between elements, and adds a step attribute to pointers to support address arithmetic on vectors and stepped arrays. The document discusses the motivation for developing C[] as a portable superset of C, describes the key features of vectors, arrays, and pointers in C[], and notes the document is composed of three parts covering motivation, language features, and the retargetable compiler.
A NOVEL FEATURE SET FOR RECOGNITION OF SIMILAR SHAPED HANDWRITTEN HINDI CHARA...cscpconf
This document describes a study that uses machine learning algorithms to recognize similar shaped handwritten Hindi characters. It extracts 85 features from each character image based on geometry. Four machine learning algorithms (Bayesian Network, RBFN, MLP, C4.5 Decision Tree) are trained on datasets containing samples of a target character pair and evaluated based on precision, misclassification rate, and model build time. Feature selection techniques are also used to reduce the feature set dimensionality before classification. Experimental results show that different algorithms perform best depending on the feature set and number of samples used for training.
Scene text recognition in mobile applications by character descriptor and str...eSAT Journals
This document presents a method for scene text recognition in mobile applications using character descriptors and structure configuration. It proposes using a character descriptor that combines feature detectors and descriptors to extract text features effectively. It also models character structure using stroke configuration maps derived from character boundaries and skeletons. The method was tested on various datasets and achieved accuracy rates above 70%, outperforming existing methods. It can detect text regions and recognize text information for applications like text understanding and retrieval on mobile devices.
Performance analysis of chain code descriptor for hand shape classificationijcga
Feature Extraction is an important task for any Image processing application. The visual properties of any image are its shape, texture and colour. Out of these shape description plays important role in any image classification. The shape description method classified into two types, contour base and region based. The contour base method concentrated on the shape boundary line and the region based method considers whole area. In this paper, contour based, the chain code description method was experimented for different hand shape.
The chain code descriptor of various hand shapes was calculated and tested with different classifier such as k-nearest- neighbour (k-NN), Support vector machine (SVM) and Naive Bayes. Principal component analysis (PCA) was applied after the chain code description. The performance of SVM was found better than k-NN and Naive Bayes with recognition rate 93%.
This document discusses syntax-directed translation and runtime environments in compiler design. It covers syntax-directed definitions, construction of syntax trees, bottom-up evaluation of S-attributed definitions, and runtime issues like storage organization, parameter passing, and symbol tables. Syntax-directed definitions associate semantic rules with context-free grammar productions to evaluate attributes during parsing. Bottom-up evaluation uses a parser stack to hold attribute values and compute new values based on reducing productions.
This document provides an overview of strings and string handling functions in C programming. It defines strings as arrays of characters terminated by a null character. Various string declaration, initialization, input, and output techniques are demonstrated. The key string manipulation functions from the string.h library - such as strlen(), strcpy(), strcat(), strcmp(), and strstr() - are described along with examples of their usage. The document aims to familiarize students with strings and enable them to utilize string concepts and functions effectively in C programming.
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A binary graphics recognition algorithm based on fitting function
1. A binary graphics recognition algorithm based on fitting
recogni
function
Lixun Peng and Dechang Pi
College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics,
Nanjing, Jiangsu, 210016, China
【Abstract】Graphics recognition is the identification of specific graphics from a print or a piece
of handwriting. When the graphics are irregular and complex, especially the recognition function is
required to classify exactly the graphics into the correct classification, and also high efficiency of
implementation is demanded at the same time, to identify them will become very complicated. In
this paper, a fitting function bases on the graphics recognition method has been proposed. The idea
is that by using some polynomial to fit for the graphics, and then compare with sample fitting
function in the template. Through the analysis of variance, the most similar template will be the
result of recognition. Though testing we find that this algorithm is effective.
【Key Words】 Pattern Recognition; graphics; images; fitting; polynomial
1 Introduction
Nowadays, the general graphics recognition algorithms base on the analysis of the dot-matrix
of the graphics. This way has significant limitations to graphics which consist of complex lines.
For example, there are specific ways to recognize Chinese characters, and so do the English words.
As a result, even the same algorithms will be realized by different programs. Especially when a
variety of types of symbols have been mixed together, most algorithms are difficult to identify
them. And it becomes more complicated if the input is a piece of handwriting.
Existing general graphics recognition algorithms, such as template matching algorithm, use
the training samples to describe the average eigenvalue, and reorganization of classifier bases on
the distance between input sample’s feature and reference characteristic of each character. Because
of the existence of a wide variety of Chinese characters fonts, and the various deformations of
handwritten Chinese characters, any feature of the documents has a distribution space. The more
distribution spaces are considered, the more precise the classify recognition will be. Hence, it’s not
enough to describe the characteristics only by the average of the eigenvalue. Another example is
Bayesian classification algorithm, which takes piecewise linear function to describe the probability
density function of the Chinese characters’ feature. In this way, the feature distribution of Chinese
characters can be identified easily. However, the distribution of eigenvalue is not a simple
statistical one. As a result, if there is no easy way to describe the probability density function, that
means all the storage space of the probability density function of any dimensional features cannot
be afforded by the practical system. Further, both these two ways don’t do well in complex
graphics classification and are also implementation inefficient.
This paper presents an algorithm, which bases on the lines fitting to the graphics. Meanwhile,
the recognition algorithm will be generated and it will describe all the complex graphics by
polynomial functions. The recognition will be done though the analysis of the similarity between
2. fitting functions and standard functions in template library. This sort of recognition is different with
currently all other methods. According to the literature search of new knowledge, we have never
seen any report based on this way of recognition. And this algorithm also contains a binary vector
graphics method at the same time.
2 relevant definitions
Definition 1 Line segment (LS) and Line Segment Set(LSS)
If a set of continuous, constituted by a number of points, and the sequence of the points can be
fitted at the same time, this will be defined as the Line Segment (this can be the curved segment or
line segment). The sequence of segments consist the Line Segment Set.
Definition: LS=((X1,Y1),(X2,Y2),...,(Xi,Yi),...,(Xn,Yn)),n is the number of the points
LSS=(LS1,LS2,..LSi,...,LSm),m is the number of the line segments
And Xi、Yi∈Z,LS∈LSS,i、n、m∈Z+,|Xi-Xi-1|<=1,|Yi-Yi-1|<=1。
Definition 2 Feature points of the line (LC) and feature points set(LCS)
For a line segment LS which consists of n points, m points will be selected from it to fit.
Every selected point is called the Feature Points of Line LS, and the sequence which is structured
by m feature points will be known as Feature Points Set.
Definition: LC∈LS。
LCS={LC1,LC2,...,LCi,…,LCm},m is the number of the feature points
And LCi∈LS,0≤i≤m≤n。
Definition 3 Best fitting function (BFF):
Make use of line feature points’ set LCS of line segment LS, through which to fit the line
segment and n-degree polynomial Y(X) or X(Y) will be obtained. Polynomial Y(X) or X(Y) will
be known as the fitting function of this line segment. Among all the functions, the best fitting one
will be defined as Best Fitting Function.
Definition: BFF(X)=An*+An-1*+…+A0*,Flag assigns 0。
BFF(Y)=An*+An-1*+…+A0*,Flag assigns 1.
And Ai∈R,i、n∈Z+
Definition 4 Best Fitting Vector (BFV)
Vector which is composed by best fitting function’s coefficient Ai and Flag of Type Y(X) or
Type X(Y) will be defined as Best Fitting Vector.
Definition: BFV=(Flag,An,An-1,…,Ai,…A0)
And Ai∈R,i、n∈Z+
Definition 5 Strokes (SK)
A set consists of best fitting functions which has similar properties will be defined as Strokes.
And it is the smallest unit of divisible element in the identifiable symbols set.
Definition: SK={BFV1,BFV2,…,BFVi,…,BFVn},n is the stroke’s template sample number
And: i,n∈Z+
For instance, the fitting vectors which are approximately paralleling X-axis in all Chinese
3. characters will form a set. And this set will be defined as “Horizontal Line”.
Definition 6 Strokes Set (SKS):
All the strokes in the symbols set which can be identified will form a set, which will be
defined as Strokes Set.
Definition: SKS={SK1,SK2,…,SKi,…,SKn},n is the number of the strokes
And i,n∈Z+
For instance, all Chinese characters are formed by “Horizontal Line”“Top-Down Vertical
Line”“Left-Downward slope line” and some other basic stroke sets.
Definition 7 Symbol Vector (SV):
If a vector which is constructed by n stroke elements, can donate a vector which to be
identified or can be identified, it will be defined as Symbol Vector. When necessary, a graphic
element can be included to record a symbol’s graphic information, in order to identify more
precisely.
Definition: SV=(SK1,SK2,…,Ski,…,SKn),n is the number of the strokes which construct this
symbol
And: i,n∈Z+
For example, Chinese character “ 十 ” is constructed by “Horizontal Line” and “Top-Down
Vertical Line” in Chinese character stroke sets. Thus, symbol “十” consists of vector (Horizontal
Line, Top-Down Vertical Line).
Definition 8 Symbols Set (SVS):
The set which is constructed by all the identifiable symbol vectors will be defined as Symbols
Set.
Definition: SVS={SV1,SV2,…,SVi,…,SVn},n is the number of identifiable symbol vectors
And i,n∈Z+
Definition 9 Knowledge Base (KB):
Stroke set’s and symbols set’s template constitute a template library, which will be defined as
Knowledge Base.
Definition: KB=<SKS,SVS>
Definition 10 Similarity Degree (Smlt):
The degree of how similar the sample to be identified and the symbols set’s template in the
knowledge base will be defined as Similarity Degree.
3 Data Structure Declaration
typedef struct{
int x,y;
}Point_Type; //definition of Point Type
typedef queue<Point_Type> Queue_Type; // Queue
typedef bool* Cov_Type; // Cover table type definition
4. typedef bool Flag_Type; // Flag of fitting type definition
typedef vector<Point_Type> LS_Type; // Line segment type definition
typedef vector<LS_Type> LSS_Type; // Line segment set type definition
typedef Point_Type LC_Type; // Feature point type definition
typedef vector<LC_Type> LCS_Type; // Feature point set type definition
typedef struct{
Flag_Type Flag; // Line segment Y(X) Type assigned 0;
Line segment X(Y) Type assigned 1;
vector<double>A; // Vector of line segment fitting function’s coefficient
long Length; // Length of line segment,it can be defined by users
Point_Type Start; // Start of line segment,it can be defined by users
LS_Type LS; // Point set of line segment,it can be defined by users
LCS_Type LCS; // Feature point set of line segment,it can be defined by users
}BFV_Type; // Best fitting vector type definition
typedef struct{
set<BFV_Type> BFVS; // Best fitting vectors which constitute the stroke
int Num; // Number of the strokes
string Name; // Name of the stroke
} SK_Type;
typedef set<SK_Type> SKS_Type; // Set of stroke type definition
typedef struct{
vector<SK_Type> SKV;//Stroke vector sets which constitute the symbol vector
bool Is_Order // Whether the stroke is written orderly
int Num; // Number of the symbol
string Name; // Name of the symbol
Bitmap Pic; // Image of the symbol,it can be defined by users
}SV_Type; // vector of the symbol
typedef set<SV> SVS_Type; // Set of symbol type definition
typedef struct{
SKS_Type SKS; // Template Library of stroke set in knowledge base
SVS_Type SVS; // Template Library of symbols set in knowledge base
}KB_Type; // Knowledge base type definition
4 Ways to solve the problems
One ways to deal with interference
A. Thresholding(Image processing)
This algorithm can only deal with binary images, so we use thresholding to create binary
images first. Thresholding algorithms have been very sophisticated,this paper will not repeat them
again.
B. Notes extraction
A sample from the handwriting must be extracted, if the lines are not thick at one pixel (key
strokes). Then recognition can be done through the algorithms introduced by this paper. The
algorithms about notes extract have been very sophisticated, this paper will not repeat them again.
5. C. Elimination buffeting in the line segment
There are several reasons may directly lead to the existence of the line segment buffetings.
Because of the delay of hardware and the problems of development environment itself, it is
possible to break the line and the split part of buffeting into a segment.
This paper will offer a simple way to eliminate buffeting. That is, we will random select some
points of a certain density in the handwriting, and connect the most adjacent ones with lines and at
the same time the distances between these points must be within a threshold. Usually, if we set the
density and the threshold reasonably, a substantial portion of the jitter in the new image can be
effectively eliminated, especially to the online input when the points’ input order can be
approached. Meanwhile, contour vectorization algorithm in the citation [6] is also a good way to
eliminate buffeting, and this paper will not repeat them again.
D. Connection of the breakpoint
When off-line identification is taken, various reasons may result in the unclear of the image.
The accuracy of this algorithm depend on the correct split of the line segment, as result, if the
original image is a line, and break points appear after recognition, and this will lead to a sharp
decline in recognition accuracy.
A lot of discussions about the treatment of this problem have been made, and this paper
provides an easy way to connect the breakpoints. Similar to the ways to eliminate of the jitters in
the line segment, we will random select some points of a certain density in the handwriting, and
connect the most adjacent ones with lines and also the distances between these points must be
within a threshold. Usually, if we set the density and the threshold reasonably, a substantial portion
of the breakpoints caused by lack of clarity in the new image can be effectively eliminated.
Two ways to deal with the spilt in line
A. On-line recognition:
(1) Initialize two cover tables CovX and CovY. They are used to record the coverage of X-
axis and Y-axis, and set the items as False (it means that they are not covered).
(2) Create two empty queues QueueX and Queue Y. The sequence of points will be pushed
as Y(X) record type and X(Y) record type into the two queues separately.
(3) Each time we get a point (Xi, Yi), we’ll check whether Xi’s and Yi’s status in CovX and
CovY is False or not. If the status of Xi in CovX is False, (Xi, Yi) will be pushed into
QueueX, and the status of Xi will be changed into True. Similarly, if the status of Yi in
CovY is False, (Xi, Yi) will be pushed into QueueY, and the status of Yi in will be
changed into True. Cycle this process until conflict occurred in one Cov table (that is, the
corresponding status of Xi or Yi the algorithm going to set has already been True). Then
the process will be moved to (4). If all the points have been haddled, the process will be
moved to (5).
(4) If conflict happened in one Cov table, all the elements in corresponding queue will be
popped and reset Cov table. Another Cov table will continue step (3) until conflict
occurred. Then pop the corresponding queue to the line segment LS [j], and clear table
Cov. For instance, if the data we get is (Xi, Yi), and status of Xi in CovX is True, the
QueueX will be cleared, and CovX will be reset as False. Meanwhile, CovY and QueueY
will continue (3) until conflict occurred in CovY. Then, elements in QueueX will be
6. popped and stored in the LS[j], and reset CovY as False, j=j+1. After all these, the
program will re-enter (3).
(5) If one line is finished, the program will enter (1) and split the next one. If there is no
more point can be obtained, the program comes to the end.
B. Off-line recognition
(1) In accordance with from left to right and from top to bottom of the scanning method,
as long as the scan to one point, similar to the seed-filling method of proliferation on this
point will be used. And all the possible lines will be connected , and then used online
recognition ways to generate LS. And then switch into (2).
(2) Each time a segment is token, it will be checked whether it has any crossing with
other segments. If there is no cross on this segment, it will be deleted from the graphics.
If there are crosses on this segment, cross-point will be added. And then, move to (1).
Repeat this step until there is no point in the graphics. Reference [6] has offered an
algorithm about how to estimate whether it has crossings with other lines. This paper will
not repeat them again.
Three Ways to choose feature points
The starting point of line segment (X1, Y1) and end point (Xn, Yn) should be selected, and the
rest of the points should base on certain rules from the various parts of the line. For instance,
every (n +1) div m points will be chosen as a feature point. And it should ensure that feature
points can describe in the outline of the original line segment.
Four Ways to handle the best fitting function:
A. ways to obtain the value of n (n is the degree of the best fitting function):
n start to fit from 1, and get the value of (fitting similarity degree). Whenever n +1 can make
increase by a threshold α, for example, α = 5%, and then get n +1 for the current n, and judge
on it until the degree of the best fitting function equals n+1 and can’t make increase by a
threshold α.
B. Ways to obtain the value of Ai(Ai is the coefficient of the best fitting function):
Ai is a coefficient, and if the absolute value is less than a threshold β. For instance, β
= 1E-3, and then delete to test whether will decrease by a threshold γ. Let’s take γ = 5% as
an example, if γ is not decreased by that much, will be removed, that is Ai equals 0. Briefly
speaking, the expression with the least degree and the least terms will be chosen to express the
highest possible fit.
Let’s take segment in Graph 1 as an example. A series of feature points will be taken, and
threshold α = 5%:
LCS={(0,0),(1,1),(2,3),(3,6),(4,10),(5,15),(6,20),(7,28),(8,34),(9,45),(10,55),(11,66),(12,79),(
13,90),(14,104)}
7. (Graph 1. red for line segment, blue for first-order fitting, green for second-order fitting)
When n = 1, after first-order fitting, the fitting function is Y1 = 7.439*X-22.58,= 936.
When n = 2, after second-order fitting, the fitting function is Y2 = 0.504*-0.626*X+0.272,=
0.999.
It is obvious that when n changes from 1 to 2, increases (0.999-0.936)/0.936=6.7%, and this
is greater than threshold 5%. Thus, the present best fitting function is Y2=0.504*-0.626*X+0.272.
(Graph 2. red for line segment, blue for first-order fitting, green for second-order fitting, black
for third-order fitting)
When n=3, after third-order fitting, the fitting function is Y3=8E-05*+0.502*+0.252,=0.999.
It can be seen that when n changes from 2 to 3, has almost not changed, and the increment is
less than threshold 5%. Thus, the best fitting function is Y2=0.504*-0.626*X+0.272.
Way 5 Ways to handle with best fitting vectors
When necessary, for example, the line segments with different length must be recognized as
8. different graphics, or the length of the line segments at different proportion of the overall situation
will affect the identification results, a length element can be added to the fitting vectors, and the
length of the line segments should be recorded. Only when it works as an image vectorization
algorithm, a start point element can be added, and record the start point of the line segment at the
same time. By these elements, the original graphics can be restored through the best fitting vectors.
For instance, the best fitting function in Graph 2 is Y2=0.504*-0.626*X+0.272. If the optional
part is not considered, it is a 3+1 dimensional vector BFV = (0,0.504,0.626,0.272).
Way 6 split the character into stroke
To split the identifiable symbols sets into stroke should base on the principle that it’s easy for
the program to practice, rather than some everyday habits.
Let’s take“ 马 ” as an example.(It’s a Chinese character.) In Chinese way of writing it, its
stroke is “┐,ㄅ,ー”. However, in order to simply the process, “┐” should be split into
.
“Horizontal Line” “ Top-Down Vertical Line” two strokes. “ㄅ” should be split into “Top-Down
Vertical Line” “Horizontal Line” “ Top-Down Vertical Line”, and the last part “丶” can be ignored.
Considering the convenience for the program, the split solution should be “Horizontal Line” “Top-
Down Vertical Line” “Top-Down Vertical Line” “Horizontal Line” “Top-Down Vertical Line”
“Horizontal Line”. Assuming that “Horizontal Line” is marked by 1, and “Top-Down Vertical Line”
is marked by 2, the result of the stroke sequence is 1 2 2 1 2 1.
Way 7 Ways to deal with similarity degree
First of all, according to the besting fitting vectors split by graphics, find the best fitting
strokes in the knowledge base’s strokes set, and generate symbol vector.
Secondly, use the best fitting symbol found by symbol vector in the knowledge base’s symbols
set. On the one hand, if the strokes are in order, the search will be fit both in strokes and order. On
the other hand, if the strokes are not in order, the order of search strokes’ number and type will not
be considered.
If not only the same number of strokes cannot be found, but also the exact same strokes cannot
be searched, the closest match principle will be adopted to find the closest symbol. For orderly
strokes character vector, then each character which matches to the maximum correct strokes
number by sequence can be adopted. If strokes are disordered, then the character whose strokes can
match a maximum number of strokes in characters to be identified will be adopted. And according
to required, character vector’s image can be obtained. By contracting the images, the result will be
confirmed.
5 The description of the algorithm
5.1.Language description:
S1: Set up knowledge base;
S2: Input samples;
S3: Samples analysis
(1) Separate the graphics of the stroke from the graphics; ?????
(2) Analyze the graphics of the stroke, categorize it into the right stroke in the stroke set;??
(3) Generate the identified symbol sequence according to whether it is in order or not;
S4: Samples recognition
(1) To find the best fitting templates in the templates bases on whether it is in order or not;
9. (2) Extract the graphics of these templates and compare them with the sample; ???
(3) Choose the best fitting graphics as recognition result;???
(4) According to the user’s need to decide whether the new sample will be restored as a
template;
S5: Output the result
5.2. Description of related parameters:
(1) LSS_Type LSS; //Line segment set type
(2) LFS_Type LFS; //Sequence type of line segment feature points set
(3) SVS_Type SVS; //Symbol sequence type
(4) SV_Type SV; //Symbol vector type
(5) KB_Type KB; //Knowledge base type
5.3. Annotation of related operation function:
bool CreatKB(); //Create knowledge base
bool LoadKB(KB_Type); //Load the knowledge base
bool StoreKB(SV_Type); //Update the knowledge base
Bitmap ReadSV(); //Input a sample to be recognized
LSS_Type GetLSS(Bitmap); //Split the line segment
LCS_Type GetLCS(LS_Type); //Generate the feature points set of line segment
BFV_Type GetBFV(LCS_Type); //Obtain best fitting vectors according to feature
points set fitting polynomial
bool AnalysisSV(SV_Type); //Analyze a symbol vector
SK_Type MatchingSK(BFV_Type); //Match the stroke by best symbol vector
SVS_Type MatchingTP(SV_Type); //Match the templates by symbol vector
SVS_Type MatchingSKV(SV_Type.SKV); //Process match according to stroke vector of
symbol vector
SVS_Type MatchingPIC(SV_Type.Pic, SVS_Type); //Process match according to
graphics of symbol
5.4. Implementation process:
S1. KB = CreatKB() or LoadKB(KB); //Create knowledge base or load knowledge base
S2. SV.Pic = ReadSV();
S3. AnalysisSV (SV) //Sample analysis
Begin
LSS = GetLSS(SV.Pic); //Split the graphics into stroke set
For i: = 1 To LSS.size() Do
Begin
//Generate feature points set
LCS = GetLCS(LSS[i]);
//Generate best fitting vector input symbol vector
SV.SKV[i].BFV.add(GetBFV(LCS));
//Write symbol vector by fitting vector matching stroke
SV.SKV[i] = MatchingSK(SV.SKV[i].BFV);
10. End
return SUCCESS;
End
S4. SVS = MatchingTP(SV); //Match the template, SVS[1] is the most probable recognition
result
Begin
//According to vector fitting, several symbol vectors can be obtained
SVS = MatchingSV(SV.SKV);
//If necessary, compare the sample graphics, and match it with every possible symbol
//graphics
SVS = MatchingPIC(SV.Pic);
If(user need to update knowledge base)
Then
StoreKB(SV);
return SVS;
End
S5. Write(SVS[0]); //Output the best fitting result
6 Algorithm Analysis
Above algorithm has been carried out on PC, and some changes can be made on the
parameters to adapt to certain circumstances according to actual situations and implementation
efficiency is easy to control. Because the matching process bases on the sequence, efficiency can be
highly improved by adding index and so on. It also has wide range of application. Whether
handwriting or printing, as long as the proper parameters and treatments are set according to actual
situation, it can be adapted to certain circumstances.
Algorithm can be improved. For instance, it can be enabled to deal with non-positive input
symbols. Chinese character is a good example. It is non-positive input, and a little bit tilt, and if we
match it directly with knowledge base will probable get a wrong result. We can rotate the
coordinate of fitting function polynomial, and by the function way, try to recognize after rotate
certain angle. If matching rate is significantly increased, it is obvious that rotate is effective.
Further, this algorithm can handle many specific symbols, not only numbers, letters,
characters and so on. We can see from Figure 3.
11. (Figure 3. Online recognition of handwriting staff of the low note)
(Upper left: handwriting sample,Upper right: feature points acquisition
lower left: eliminate buffeting,lower right: line segment split)
7 Conclusion
This paper presents a graphic recognition algorithm bases on fitting functions. And its
effectiveness has been verified by the recognition of handwriting musical symbol. Through fitting
the graphic by several polynomials, and compare it with samples in template library. And after
analysis, the templates with highest similarity will be extracted as recognition result. According to
the recognition way in this paper, it requires the input symbol should be in standard direction. For
non-positive input symbols and symbols which vectors’ differences are not very apparent, the result
is not very accurate. Thus, this system need further improvement, and make it more pratical.
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