Hot Dog! A History free essay sample

An in depth history of the hot dog, and discussion of hot dog related issues. i.e. hot dog to bun ratio conspiracy. Hot Dog! a History This is a research paper written about the history and reputation of the hot dog. It discusses the origin of the hot dog, origin of the word hot dog, and discusses various aspects and impacts of the hot dogs existence in todays American culture. Although hot dogs originated in Germany, hot dogs are still as American as apple pie. Hot dogs are one of the oldest forms of processed food, having been mentioned in Homers Odyssey as far back as the 9th Century B.C. (Jackson) At first glance one might think that the Hot Dog has a rather dry history. Upon prying into the archives and prying into the taste buds of the masses, I discovered the hot dogs meaty and controversial history and prominent present gives people plenty to say about the 500 year old dog. We will write a custom essay sample on Hot Dog! A History or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page

Elton John Billy Joel free essay sample

Amazing but not surprising, 50,000 fans on a Sunday night and 50,000 fans on Monday night crammed into Foxboro Stadium to show their appreciation to piano prodigies Elton John and Billy Joel; this wasnt just a concert, it was an experience. For four hours, the fans were up and singing as Joel and John performed Your Song, Honesty, and Dont Let the Sun Go Down on Me. Billys band left, leaving Elton to entertain with such hits as Philadelphia Freedom, Rocket Man, The One, and more. Once more, the two joined to sing I Guess Thats Why They Call It the Blues. Then Billy serenaded thousands with Scenes From an Italian Restaurant, River of Dreams, Only the Good Die Young. The crowd spread into the aisles, on top of seats and bleachers, and screamed, sang, clapped, arranged a wave, and had a once-in-a-lifetime musical experience. Its no surprise that the two could put on such an enjoyable show and leave with every single face, in what seemed to be an endless crowd, smiling and singing while walking to the parking lots, cooking on hibachis, saluting their favorite singers. We will write a custom essay sample on Elton John Billy Joel or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page An intense concert where almost everyone was closely familiar with the songs, it was hours of talent, brilliance, entertainment, perhaps the best concert to hit this country in decades. Why didnt these two superstars tour earlier? Well, better late than never and in this case, it really was better. Eltons conservative attitude roused many standing ovations and while his fingers skimmed over the keys, he smiled at the crowd, his eyes dancing, his boyish facial expressions classic to his personality. Though, to many peoples disappointment, there were no costumes, only sparkles and leopard-print pants. Billy, on the other hand, as some bluntly put it, was crazy, out of control, like many of those who watched in the bleachers. He excitedly sprayed himself with bug spray, explaining These suckers can fly right into your mouth. He banged on the piano at times, while at others, he, too, sat and belted out his favorite tunes, then bowed in front of the audience. In River of Dreams, he did half a cartwheel off the top of the piano, waltzed around the stage with what appeared to be a big tube on his head, and jumped into the first row of people. Elton would do no such thing, but he definitely oohed and aahed the audience as well. Two so diverse artists, but two so very talented. All in all, this was a concert that will linger in the memory of anyone who attended. Adjectives cant describe the entertainment, excitement and energy these two performers delivered to such a huge number of people. Their voices merged as if they had been singing together for decades, and after they left the stage and hopped into limousines that whisked them off, there was a sense of sadness that lingered it was over! No one thought it would actually end, even as 50,000 people joined in on Piano Man as Billy and Elton merely listened and then began to play again. Yes, it was over, but it will remain as an experience to rave about and never to forget. .

The Entrepreneurial Team in Business Plan Process Essay Example

The Entrepreneurial Team in Business Plan Process Paper 1Introduction Business Link, describes Business Plan as a â€Å"roadmap for future development† and has an essential role for every enterprises. The document narrates â€Å"a business, its objectives, its strategies, the market it is in and its financial forecasts† and it serves several functions to business unit from securing external funding to measuring success within the business (2008). As a statement of intent, business plan displays â€Å"where you are now and where you want to go† (Cracknell, 2006) and a growth strategy has to be incorporated to turn the business plan from a static document into a dynamic template that promote significant growth instead of survival, and more importantly, driven by people. 2Identification of the entrepreneurial team The section of Management Team in the business plan contains description of the roles and explicit functions of the members represented by an organizational chart that include the present force, or otherwise numbered order of people who are anticipated to join or hire with realistic allocated budget (Timmons and Spinelli, p. 243). Prudent entrepreneurs will examine during the business plan process to diagnose current and potential skills’ gaps to execute the plan. We will write a custom essay sample on The Entrepreneurial Team in Business Plan Process specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on The Entrepreneurial Team in Business Plan Process specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on The Entrepreneurial Team in Business Plan Process specifically for you FOR ONLY $16.38 $13.9/page Hire Writer The team is not only confined to management level, but also employees who are empowered to run the daily activities of the venture (Vecchio, 2007). The manpower budgets are guiding tool of the team structure comprising of estimates and total force needed. Identification process involves matching against the job description of each placement with induction before, during and after implementation to avoid the risk of â€Å"overtrading† during startup (Ogilvie, 2006). 2. 1The Team members Managing team dynamic is ever a complex issue between plan and â€Å"realworld†. To VC, â€Å"ideas are a dime a dozen† and it is the execution skills that counts. A venture should begin with resumes of all people involved consisting of the past and track records of the team to ensure capability to meet the projected milestone and future success. They are concise in the business plan. Without a right team, none of the other parts of the business plan really matters (Sahlman, 1997). However, the limitation of early-stage management teams is common in a lead entrepreneur or a small group of founders in small enterprise. During the identification process, focus on strengths of current management team and realistic outline for addition of future officers are reflected in the business plan (McLeod, 2004). The members from the management team are expected be a self-directed work teams who are able to empower employees to make decisions about their work and to help steer corporate vision (Budwig, 2008). In the business plan compilation, the key management team is identified with consideration of management compensation and ownership (p. 243). 2. The Board of Directors In an investor-owned firm (IOF), the composition of the board has to be elected. The choices of the directors are troublesome for new venture and worth careful thought in the identification process (p. 345). The decision of choice is either internal or external which will start with identifying the missing relevant experience to close the potential skill gap, know how, networks and the necessity of hiring from external source. The board is likely to be comprised of mixture of executive directors and non-executive directors. The directors are key value drivers; hence, decisions have to be objective to select trustworthy people. Accordingly, it is not uncommon that directors of new ventures are either from the founder’s team, nominated from internal source, or from internal network unless skill gaps exist or representation to bring value of credibility into the venture is essential. 2. 3The Value-added Investors In empirical study by Palliam (2005), where external funds are required, the main source is equity rather than debt in a bridged pecking order from self-funding to external equity in preference over bank finance. This is because debts are personal liability as it invariably requires to be underwritten by personal guarantees carrying distress cost of bankruptcy. Capital budgeting and deal offers are integral part of the financial analysis section which is apparently comprehensible to the entrepreneurs who are promoters of the venture and having awareness of synergies with suitable business fit deriving from potential group of investors or institutions. Other than cash, identify the correct investors from an ideal business fit can significantly enhance value of the collaboration in tapping into the investors’ resources of experience, wisdom and networks. Likewise, it adds â€Å"devil’s advocate† into the venture idea to identify new area of opportunity and avoidance of the mousetrap fallacy (p. 122). The pre and post money valuation are be presented to pitch and induce interest from the potential investors whose appetite differs. However, bias from information asymmetry in the capital market (Storey, 2005) has its deterrence to identification process in convincing a greenfield project of early stage venture. 3Conclusion In Timmons Model, entrepreneurial team is an indispensible ingredient to potential venture and great teams are short in such endeavours (p. 91). According to Fitz-Enz, employees cost exceeds 40% of corporate expense and that people, and not cash, buildings or equipment, are the livelihood of business (2000, p. 1), therefore the drivers of the entire value chain of business are rich in human interaction. Evidence in Manson and Stark has emphasized investors’ interest in looking for the right people â€Å"who are honest, exhibit a strong work ethic, understand what it takes to make the business succeed, have invested in their business, and have a realistic notion of how to value the business† (Mason and Stark, 2004). A business plan can set the foundation of rising new capital with subsequent profitably in operation. Those businesses that succeeded have identified the unique preposition of their products, territories or markets and have tailored programs to net results from the opportunity identified. All these activities are performed and driven by the entrepreneurial team in the course of implementation and in most cases, supported by the right alliances from the capital market. Eventually, without the people from the team and the financial industry, the entrepreneur’s road to success is skeptical. Reference : Abrams, R. (2003). ‘The Successful Business Plan: Secrets and Strategies’. Palo Alto, CA: The Planning ShopTM. Budwig, M. (2008). ‘Self-Directed Work Teams for Technical Communication: Best Practices in Management’. Society for Technical Communication, 55th Annual Conference, June 2, 2008. Available from: http://www. stc. org/edu/55thConf/download. asp? ID=123 (Accessed on 6 July 2008). Business link (2008). ‘Use your business plan to get funding’. London, U. K. : The Commissioners for Revenue Customs (HMRC). Available from: http://www. businesslink. gov. uk/bdotg/action/layer? topicId=1073958998r. s=sl (Accessed on 6 July 2008). Cracknell, H. (2006). ‘Business Link Guide to writing a Business Plan’. Bytestart. co. uk. (the small business portal), July 26, 2006. Available from: http://www. bytestart. co. uk/content/businessplans/30_2/business-link-business-plan. html (Accessed on 6 July 2008). Fitz-Enz, J. (2000). ‘ROI of Human Capital: Measuring the Economic Value of Employee Performance’. New York, NY: AMACOM Div American Mgmt Assn. http://www. emeraldinsight. com. ezproxy. liv. ac. uk/Insight/ViewContentServlet? Filename=Published/EmeraldFullTextArticle/Articles/2940060404. html (Accessed on 6 July 2008). Mason, C. and Stark, M. (2004). ‘What do Investors Look for in a Business Plan? : A Comparison of the Investment Criteria of Bankers, Venture Capitalists and Business Angels’. International Small Business Journal, 22(3), p. 227. Available from: http://isb. sagepub. com. ezproxy. liv. ac. uk/cgi/reprint/22/3/227 (Accessed on 6 July 2008). McLeod, R. (2004). ‘Management Team’. Glasgow, U. K. : The Scottish Institute for Enterprise. Available from: http://www. sie. ac. uk/File/43R0. aspx (Accessed on 6 July 2008). Ogilvie, J. (2006). ‘CIMA Learning System 2007 Management Accounting Financial Strategy (Cima Learning Systems Strategic Level 2007)’. Burlington, MA : Elsevier Ltd. Palliam, R. (2005). ‘Estimating the cost of capital: considerations for small business’. The Journal of Risk Finance, 6(4), p. 335-340. Available from: Sahlman, W. A. (1997). ‘How to Write a Great Business Plan’. Harvard Business Review Article, July 1, 1997. Available from: www. uio. no/studier/emner/matnat/sfe/ENT4000/v05/undervisningsmateriale/Forretningsplan. pdf (Accessed on 6 July 2008). Storey, D. (2005) Understanding the Small Business Sector. London, U. K. : Thomson Learning. Timmons, J. A. and Spinelli, S. (2007). â€Å"New Venture Creation: Entrepreneurship for the 21st Century†. New York, N. Y. : McGraw-Hill/Irwin. Vecchio, R. P. (2006). ‘Organizational behavior: Core concepts’. Mason, OH: Thomson South-Western.

Logical Intervention in Vietna essays

Logical Intervention in Vietna essays American military intervention in Vietnam has always been a highly controversial issue. Many Americans were against intervention in Vietnam. Many Americans question rather the United States should have ever sent troops to Southeast Asia, and if intervention was the correct action, based on foreign policy and bipartisan ideas. After observation of these ideas, it is safe to say that foreign policy and bipartisan ideas justified American military intervention. First of all the United States stance on foreign policy needs to be observed. After World War II the United States became the most powerful nation, it had ever been. The United States had ideas of spreading the American values of liberty, equality, and democracy throughout the world.1 During this time the Soviet Union was less focused on spreading their Communist ideas, and more focused on rebuilding there country, which was demolished during World War II.2 As unthreatening as this seems now, the United States had a great distrust of Russia. The American peoples anti-Nazi sentiments, of World War II, began to be anti-Soviet. The United States and the Soviet Unions conflicting aims and equal distrust were two of the main reason the Cold War began. All Americans, Democrats and Republicans, were allied against the Soviet Union. The Democratic president Truman and his predecessor Eisenhower, a Republican, were both anti-Communist and anti-Soviet. Another policy at this time was containment. The policy was defined by George F. Kennan.3 In a nut shell, the policy stated that Americans should try to stop the spread of Communism. This policy, directed mainly at the Soviet Union, used by the all of the Presidents, though given different monikers, from 1946 until after the Vietnam War. During these times, from about 1945 to 1954, the Vietnamese were fighting a war against France. The Vietnamese sought independence while the Frenc ...

America as a Christian Nation Essay Example | Topics and Well Written Essays - 1000 words

America as a Christian Nation - Essay Example And that they do it only by necessity. According to him "injustice is more profitable to an individual than justice." (360 B.C.) However, it has been a popular teaching in Christianity that God gave men free will. And according to C.S Lewis, it is that same free will that made evil possible. (1943) Men are then free to do good as well as evil. Therefore, because of this free will, in my opinion, it doesn't matter whether or not men are born just or not. Nor is the reason why men are unjust, whether by whim or necessity. All that matters is that men have the capacity to be unjust. And it is that capability for injustice that should be constrained, but how It has been argued that the best of all things is "to do injustice and not be punished," the worst is "to suffer injustice without power of retaliation," (Devine 2004) hence the middle ground is what we call justice as imposed by government. And I agree. I believe that it is the existence of the government that constrains this capability of evil. It provides for laws, renders judgment and imposes punishment. I agree with Rousseau when he stated that even if God did not have a hand on the legitimacy of the government, it is God that gave the individuals inalienable rights. (Devine, 2004) And this includes the right to form a unified body to govern them. You need not be Christian. You can be Muslim or Jewish. You may even call your God different names. But I think, if one believes that all men has inalienable rights, he or she must believe that there is a higher power bestowing that right. Because if the contrary was true, that there is no higher power, then men would be the highest power. We are all gods. I don't think that's right. If we are all gods, that would lead to chaos. As such, since the power of the American government emanates from the people, America as a nation must believe in a higher power, a religion. Why Christianity One main reason why America leans towards Christianity more than the other religions is because of history. We have learned in the readings that through conquests, Christianity was spread from Europe, to the American continent. However, there are also other religions brought by immigrants inside America. There is also the fact that there are many who prefers not to believe in any religion at all. But despite that, I still believe that America is still a Christian nation. Why Because of its teachings. No matter if you call yourself an atheist or declare that you believe in some other god, if you practice what Christianity preach, I call you a Christian. So what are these teachings Firstly, Christianity teaches us to be just, to respect and to do no harm to another. These are the basic virtues that lead to the very goal of our Constitution: the protection of life, liberty and property. Secondly, Christianity t

Evocative Production about Aging Care Article Example | Topics and Well Written Essays - 500 words

Evocative Production about Aging Care - Article Example Gaugler et al. observed that the test for civilization in any society is the manner in which it takes care of its frail members. The policy for the aged in Australia safeguards old people in the society from fearing the young as well as feeling misplaced. If the aged are offered the necessary attention and care, their life is prolonged and opportunistic ailments in old age may be avoided thereby reducing the cost of care as well as the workload of the caregivers. It is important to teach children and the youth that the aging was young just like them and that they too are headed for old age. Sometimes the old might be perceived as having little contribution to the society’s advancement in terms of wealth creation and social development. Nevertheless, the young need to appreciate the efforts of those who came before them in the maintenance of an enabling environment for them to act. For example, if the aged polluted the environment and caused the depletion of natural resources available at this particular time, the youth could not enjoy living on earth as they do. Naturally, an old person who is ill-treated in the society visualizes it as the young population taking advantage of the weak. Baxter cautioned that the society should not let their aging population regret their own existence. Rather, they should be made to feel loved and useful in community affairs. Furthermore, their experience is critical in molding and instilling good morals to the young. The natural wisdom they possess as a result of having interacted and worked with diverse people for a long time may have a significant impact on the conduct of future generations. Stigmatization of the aged may result in the loss of a resourceful component of the society since their capacity to actively participate in communal activities.

Handwritten Character Recognition Using Bayesian Decision Theory

Handwritten Character Recognition Using Bayesian Decision Theory Abstract: Character recognition (CR) can solve more complex problem in handwritten character and make recognition easier. Handwriting character recognition (HCR) has received extensive attention in academic and production fields. The recognition system can be either online or offline. Offline handwritten character recognition is the sub fields of optical character recognition (OCR). The offline handwritten character recognition stages are preprocessing, segmentation, feature extraction and recognition. Our aim is to improve missing character rate of an offline character recognition using Bayesian decision theory. Keywords: Character recognition, Optical character recognition, Off-line Handwriting, Segmentation, Feature extraction, Bayesian decision theory. Introduction The recognition system can be either on-line or off-line. On-line handwriting recognition involves the automatic conversion of text as it is written on a special digitized or PDA, where a sensor picks up the pen-tip movements as well as pen-up/pen-down switching. That kind of data is known as digital ink and can be regarded as a dynamic representation of handwriting. Off-line handwriting recognition involves the automatic conversion of text in an image into letter codes which are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting. The aim of character recognition is to translate human readable character to machine readable character. Optical character recognition is a process of translation of human readable character to machine readable character in optically scanned and digitized text. Handwritten character recognition (HCR) has received extensive attention in academic and production fields. Bayesian decision theory is a fundamental statistical approach that quantifies the tradeoffs between various decisions using probabilities and costs that accompany such decision. They divided the decision process into the following five steps: Identification of the problem. Obtaining necessary information. Production of possible solution. Evaluation of such solution. Selection of a strategy for performance. They also include a sixth stage implementation of the decision. In the existing approach missing data cannot be recognition which is useful in recognition historical data. In our approach we are recognition the missing words using Bayesian classifier. It first classifier the missing words to obtain minimize error. It can recover as much error as possible. Related Work The history of CR can be traced as early as 1900, when the Russian scientist Turing attempted to develop an aid for the visually handicapped [1]. The first character recognizers appeared in the middle of the 1940s with the development of digital computers. The early work on the automatic recognition of characters has been concentrated either upon machine-printed text or upon a small set of well-distinguished handwritten text or symbols. Machine-printed CR systems in this period generally used template matching in which an image is compared to a library of images. For handwritten text, low-level image processing techniques have been used on the binary image to extract feature vectors, which are then fed to statistical classifiers. Successful, but constrained algorithms have been implemented mostly for Latin characters and numerals. However, some studies on Japanese, Chinese, Hebrew, Indian, Cyrillic, Greek, and Arabic characters and numerals in both machine-printed and handwritten cas es were also initiated [2]. The commercial character recognizers were available in the 1950s, when electronic tablets capturing the x-y coordinate data of pen-tip movement was first introduced. This innovation enabled the researchers to work on the on-line handwriting recognition problem. A good source of references for on-line recognition until 1980 can be found in [3]. Studies up until 1980 suffered from the lack of powerful computer hardware and data acquisition devices. With the explosion of information technology, the previously developed methodologies found a very fertile environment for rapid growth addition to the statistical methods. The CR research was focused basically on the shape recognition techniques without using any semantic information. This led to an upper limit in the recognition rate, which was not sufficient in many practical applications. Historical review of CR research and development during this period can be found in [4] and [3] for off-line and on-line cases, respectively. The real progress on CR systems is achieved during this period, using the new development tools and methodologies, which are empowered by the continuously growing information technologies. In the early 1990s, image processing and pattern recognition techniques were efficiently combined with artificial intelligence (AI) methodologies. Researchers developed complex CR algorithms, which receive high-resolution input data and require extensive number crunching in the implementation phase. Nowadays, in addition to the more powerful computers and more accurate electronic equipments such as scanners, cameras, and electronic tablets, we have efficient, modern use of methodologies such as neural networks (NNs), hidden Markov models (HMMs), fuzzy set reasoning, and natural language processing. The recent systems for the machine-printed off-line [2] [5] and limited vocabulary, user-dependent on-line handwritten characters [2] [12] are quite satisfactory for restricted applications. However, there is still a long way to go in order to reach the ultimate goal of machine simulation of fluent human reading, especially for unconstrained on-line and off-line handwriting. Bayesian decision Theory (BDT), one of the statistical techniques for pattern classification, to identify each of the large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within 20 fonts was randomly distorted to produce a file of 20,000 unique instances [6]. Existing System In this overview, character recognition (CR) is used as an umbrella term, which covers all types of machine recognition of characters in various application domains. The overview serves as an update for the state-of-the-art in the CR field, emphasizing the methodologies required for the increasing needs in newly emerging areas, such as development of electronic libraries, multimedia databases, and systems which require handwriting data entry. The study investigates the direction of the CR research, analyzing the limitations of methodologies for the systems, which can be classified based upon two major criteria: 1) the data acquisition process (on-line or off-line) and 2) the text type (machine-printed or handwritten). No matter in which class the problem belongs, in general, there are five major stages Figure1 in the CR problem: 1) Preprocessing 2) Segmentation 3) Feature Extraction 4) Recognition 5) Post processing 3.1. Preprocessing The raw data, depending on the data acquisition type, is subjected to a number of preliminary processing steps to make it usable in the descriptive stages of character analysis. Preprocessing aims to produce data that are easy for the CR systems to operate accurately. The main objectives of preprocessing are: 1) Noise reduction 2) Normalization of the data 3) Compression in the amount of information to be retained. In order to achieve the above objectives, the following techniques are used in the preprocessing stage. Preprocessing Segmentation Splits Words Feature Extraction Recognition Post processing Figure 1. Character recognition 3.1.1 Noise Reduction The noise, introduced by the optical scanning device or the writing instrument, causes disconnected line segments, bumps and gaps in lines, filled loops, etc. The distortion, including local variations, rounding of corners, dilation, and erosion, is also a problem. Prior to the CR, it is necessary to eliminate these imperfections. Hundreds of available noise reduction techniques can be categorized in three major groups [7] [8]: a) Filtering b) Morphological Operations c) Noise Modeling 3.1.2 Normalization Normalization methods aim to remove the variations of the writing and obtain standardized data. The following are the basic methods for normalization [4] [10][16]. a) Skew Normalization and Baseline Extraction b) Slant Normalization c) Size Normalization 3.1.3 Compression It is well known that classical image compression techniques transform the image from the space domain to domains, which are not suitable for recognition. Compression for CR requires space domain techniques for preserving the shape information. a) Threshold: In order to reduce storage requirements and to increase processing speed, it is often desirable to represent gray-scale or color images as binary images by picking a threshold value. Two categories of threshold exist: global and local. Global threshold picks one threshold value for the entire document image which is often based on an estimation of the background level from the intensity histogram of the image. Local (adaptive) threshold use different values for each pixel according to the local area information. b) Thinning: While it provides a tremendous reduction in data size, thinning extracts the shape information of the characters. Thinning can be considered as conversion of off-line handwriting to almost on-line like data, with spurious branches and artifacts. Two basic approaches for thinning are 1) pixel wise and 2) nonpareil wise thinning [1]. Pixel wise thinning methods locally and iteratively process the image until one pixel wide skeleton remains. They are very sensitive to noise and may deform the shape of the character. On the other hand, the no pixel wise methods use some global information about the character during the thinning. They produce a certain median or centerline of the pattern directly without examining all the individual pixels. In clustering-based thinning method defines the skeleton of character as the cluster centers. Some thinning algorithms identify the singular points of the characters, such as end points, cross points, and loops. These points are the source of problems. In a nonpareil wise thinning, they are handled with global approaches. A survey of pixel wise and nonpareil wise thinning approaches is available in [9]. 3.2. Segmentation The preprocessing stage yields a clean document in the sense that a sufficient amount of shape information, high compression, and low noise on a normalized image is obtained. The next stage is segmenting the document into its subcomponents. Segmentation is an important stage because the extent one can reach in separation of words, lines, or characters directly affects the recognition rate of the script. There are two types of segmentation: external segmentation, which is the isolation of various writing units, such as paragraphs, sentences, or words, and internal segmentation, which is the isolation of letters, especially in cursively written words. 1) External Segmentation: It is the most critical part of the document analysis, which is a necessary step prior to the off-line CR Although document analysis is a relatively different research area with its own methodologies and techniques, segmenting the document image into text and non text regions is an integral part of the OCR software. Therefore, one who works in the CR field should have a general overview for document analysis techniques. Page layout analysis is accomplished in two stages: The first stage is the structural analysis, which is concerned with the segmentation of the image into blocks of document components (paragraph, row, word, etc.), and the second one is the functional analysis, which uses location, size, and various layout rules to label the functional content of document components (title, abstract, etc.) [12]. 2) Internal Segmentation: Although the methods have developed remarkably in the last decade and a variety of techniques have emerged, segmentation of cursive script into letters is still an unsolved problem. Character segmentation strategies are divided into three categories [13] is Explicit Segmentation, Implicit Segmentation and Mixed Strategies. 3.3. Feature Extraction Image representation plays one of the most important roles in a recognition system. In the simplest case, gray-level or binary images are fed to a recognizer. However, in most of the recognition systems, in order to avoid extra complexity and to increase the accuracy of the algorithms, a more compact and characteristic representation is required. For this purpose, a set of features is extracted for each class that helps distinguish it from other classes while remaining invariant to characteristic differences within the class[14]. A good survey on feature extraction methods for CR can be found [15].In the following, hundreds of document image representations methods are categorized into three major groups are Global Transformation and Series Expansion, Statistical Representation and Geometrical and Topological Representation . 3.4. Recognition Techniques CR systems extensively use the methodologies of pattern recognition, which assigns an unknown sample into a predefined class. Numerous techniques for CR can be investigated in four general approaches of pattern recognition, as suggested in [16] are Template matching, Statistical techniques, and Structural techniques and Neural networks. 3.5. Post Processing Until this point, no semantic information is considered during the stages of CR. It is well known that humans read by context up to 60% for careless handwriting. While preprocessing tries to clean the document in a certain sense, it may remove important information, since the context information is not available at this stage. The lack of context information during the segmentation stage may cause even more severe and irreversible errors since it yields meaningless segmentation boundaries. It is clear that if the semantic information were available to a certain extent, it would contribute a lot to the accuracy of the CR stages. On the other hand, the entire CR problem is for determining the context of the document image. Therefore, utilization of the context information in the CR problem creates a chicken and egg problem. The review of the recent CR research indicates minor improvements when only shape recognition of the character is considered. Therefore, the incorporation of contex t and shape information in all the stages of CR systems is necessary for meaningful improvements in recognition rates. The proposed System Architecture The proposed research methodology for off-line cursive handwritten characters is described in this section as shown in Figure 2. 4.1 Preprocessing There exist a whole lot of tasks to complete before the actual character recognition operation is commenced. These preceding tasks make certain the scanned document is in a suitable form so as to ensure the input for the subsequent recognition operation is intact. The process of refining the scanned input image includes several steps that include: Binarization, for transforming gray-scale images in to black white images, scraping noises, Skew Correction- performed to align the input with the coordinate system of the scanner and etc., The preprocessing stage comprise three steps: (1) Binarization (2) Noise Removal (3) Skew Correction Scanned Document Image Feature Extraction Bayesian Decision Theory Training and Recognition Pre-processing Binarization Noise Removal Skew correction Segmentation Line Word Character Recognition o/p Figure 2. Proposed System Architecture 4.1.1 Binarization Extraction of foreground (ink) from the background (paper) is called as threshold. Typically two peaks comprise the histogram gray-scale values of a document image: a high peak analogous to the white background and a smaller peak corresponding to the foreground. Fixing the threshold value is determining the one optimal value between the peaks of gray-scale values [1]. Each value of the threshold is tried and the one that maximizes the criterion is chosen from the two classes regarded as the foreground and back ground points. 4.1.2 Noise Removal The presence of noise can cost the efficiency of the character recognition system; this topic has been dealt extensively in document analysis for typed or machine-printed documents. Noise may be due the poor quality of the document or that accumulated whilst scanning, but whatever is the cause of its presence it should be removed before further Processing. We have used median filtering and Wiener filtering for the removal of the noise from the image. 4.1.3 Skew Correction Aligning the paper document with the co-ordinate system of the scanner is essential and called as skew correction. There exist a myriad of approaches for skew correction covering correlation, projection, profiles, Hough transform and etc. For skew angle detection Cumulative Scalar Products (CSP) of windows of text blocks with the Gabor filters at different orientations are calculated. Alignment of the text line is used as an important feature in estimating the skew angle. We calculate CSP for all possible 50X50 windows on the scanned document image and the median of all the angles obtained gives the skew angle. 4.2 Segmentation Segmentation is a process of distinguishing lines, words, and even characters of a hand written or machine-printed document, a crucial step as it extracts the meaningful regions for analysis. There exist many sophisticated approaches for segmenting the region of interest. Straight-forward, may be the task of segmenting the lines of text in to words and characters for a machine printed documents in contrast to that of handwritten document, which is quiet difficult. Examining the horizontal histogram profile at a smaller range of skew angles can accomplish it. The details of line, word and character segmentation are discussed as follows. 4.2.1 Line Segmentation Obviously the ascenders and descanters frequently intersect up and down of the adjacent lines, while the lines of text might itself flutter up and down. Each word of the line resides on the imaginary line that people use to assume while writing and a method has been formulated based on this notion shown fig.3. Figure 3. Line Segmentation The local minima points are calibrated from each Component to approximate this imaginary baseline. To calculate and categorize the minima of all components and to recognize different handwritten lines clustering techniques are deployed. 4.2.2 Word and Character Segmentation The process of word segmentation succeeds the line separation task. Most of the word segmentation issues usually concentrate on discerning the gaps between the characters to distinguish the words from one another other. This process of discriminating words emerged from the notion that the spaces between words are usually larger than the spaces between the characters in fig 4. Figure 4. Word Segmentation There are not many approaches to word segmentation issues dealt in the literature. In spite of all these perceived conceptions, exemptions are quiet common due to flourishes in writing styles with leading and trailing ligatures. Alternative methods not depending on the one-dimensional distance between components, incorporates cues that humans use. Meticulous examination of the variation of spacing between the adjacent characters as a function of the corresponding characters themselves helps reveal the writing style of the author, in terms of spacing. The segmentation scheme comprises the notion of expecting greater spaces between characters with leading and trailing ligatures. Recognizing the words themselves in textual lines can itself help lead to isolation of words. Segmentation of words in to its constituent characters is touted by most recognition methods. Features like ligatures and concavity are used for determining the segmentation points. 4.3 Feature Extraction The size inevitably limited in practice, it becomes essential to exploit optimal usage of the information stored in the available database for feature extraction. Thanks to the sequence of straight lines, instead of a set of pixels, it is attractive to represent character images in handwritten character recognition. Whilst holding discriminated information to feed the classifier, considerable reduction on the amount of data is achieved through vector representation that stores only two pairs of ordinates replacing information of several pixels. Vectorization process is performed only on basis of bi-dimensional image of a character in off-line character recognition, as the dynamic level of writing is not available. Reducing the thickness of drawing to a single pixel requires thinning of character images first. Character before and after Thinning After streamlining the character to its skeleton, entrusting on an oriented search process of pixels and on a criterion of quality of represe ntation goes on the vectorization process. The oriented search process principally works by searching for new pixels, initially in the same direction and on the current line segment subsequently. The search direction will deviate progressively from the present one when no pixels are traced. The dynamic level of writing is retrieved of course with moderate level of accuracy, and that is object of oriented search. Starting the scanning process from top to bottom and from left to right, the starting point of the first line segment, the first pixel is identified. According to the oriented search principle, specified is the next pixel that is likely to be incorporated in the segment. Horizontal is the default direction of the segment considered for oriented search. Either if the distortion of representation exceeds a critical threshold or if the given number of pixels has been associated with the segment, the conclusion of line segment occurs. Computing the average distance between the l ine segment and the pixels associated with it will yield the distortion of representation. The sequence of straight lines being represented through ordinates of its two extremities character image representation is streamlined finally. All the ordinates are regularized in accordance to the initial width and height of character image to resolve scale Variance. 4.4 Bayesian Decision Theories The Bayesian decision theory is a system that minimizes the classification error. This theory plays a role of a prior. This is when there is priority information about something that we would like to classify. It is a fundamental statistical approach that quantifies the tradeoffs between various decisions using probabilities and costs that accompany such decisions. First, we will assume that all probabilities are known. Then, we will study the cases where the probabilistic structure is not completely known. Suppose we know P (wj) and p (x|wj) for j = 1, 2à ¢Ã¢â€š ¬Ã‚ ¦n. and measure the lightness of a fish as the value x. Define P (wj |x) as the a posteriori probability (probability of the state of nature being wj given the measurement of feature value x). We can use the Bayes formula to convert the prior probability to the posterior probability P (wj |x) = Where p(x) P (x|wj) is called the likelihood and p(x) is called the evidence. Probability of error for this decision P (w1 |x) if we decide w2 P (w2|x) if we decide w1 P (error|x) = { Average probability of error P (error) = P (error) = Bayes decision rule minimizes this error because P (error|x) = min {P (w1|x), P (w2|x)} Let {w1. . . wc} be the finite set of c states of nature (classes, categories). Let {ÃŽÂ ±1. . . ÃŽÂ ±a} be the finite set of a possible actions. Let ÃŽÂ » (ÃŽÂ ±i |wj) be the loss incurred for taking action ÃŽÂ ±i when the state of nature is wj. Let x be the D-component vector-valued random variable called the feature vector. P (x|wj) is the class-conditional probability density function. P (wj) is the prior probability that nature is in state wj. The posterior probability can be computed as P (wj |x) = Where p(x) Suppose we observe x and take action ÃŽÂ ±i. If the true state of nature is wj, we incur the loss ÃŽÂ » (ÃŽÂ ±i |wj). The expected loss with taking action ÃŽÂ ±i is R (ÃŽÂ ±i |x) = which is also called the conditional risk. The general decision rule ÃŽÂ ±(x) tells us which action to take for observation x. We want to find the decision rule that minimizes the overall risk R = Bayes decision rule minimizes the overall risk by selecting the action ÃŽÂ ±i for which R (ÃŽÂ ±i|x) is minimum. The resulting minimum overall risk is called the Bayes risk and is the best performance that can be achieved. 4.5 Simulations This section describes the implementation of the mapping and generation model. It is implemented using GUI (Graphical User Interface) components of the Java programming under Eclipse Tool and Database storing data in Microsoft Access. For given Handwritten image character and convert to Binarization, Noise Remove and Segmentation as shown in Figure 5(a). Then after perform Feature Extraction, Recognition using Bayesian decision theory as shown in Figure5(b). Figure 5(a) Binarization, Noise Remove and Segmentation Figure 5(b) Recognition using Bayesian decision theory 5. Results and Discussion This database contains 86,272 word instances from an 11,050 word dictionary written down in 13,040 text lines. We used the sets of the benchmark task with the closed vocabulary IAM-OnDB-t13. There the data is divided into four sets: one set for training; one set for validating the Meta parameters of the training; a second validation set which can be used, for example, for optimizing a language model; and an independent test set. No writer appears in more than one set. Thus, a writer independent recognition task is considered. The size of the vocabulary is about 11K. In our experiments, we did not include a language model. Thus the second validation set has not been used. Table1. Shows the results of the four individual recognition systems [17]. The word recognition rate is simply measured by dividing the number of correct recognized words by the number of words in the transcription. We presented a new Bayesian decision theory for the recognition of handwritten notes written on a whiteboard. We combined two off-line and two online recognition systems. To combine the output sequences of the recognizers, we incrementally aligned the word sequences using a standard string matching algorithm. Evaluation of proposed Bayesian decision theory with existing recognition systems with respect to graph is shown in figure 6. Table 1. Results of four individuals recognition systems System Method Recognition rate Accuracy 1st Offline Hidden Markov Method 66.90% 61.40% 1st Online ANN 73.40% 65.10% 2nd Online HMM 73.80% 65.20% 2nd Offline Bayesian Decision theory 75.20% 66.10% Figure 6 Evaluation of Bayesian decision theory with existing recognition systems Then each output position the word with the most occurrences has been used as the  ¬Ã‚ nal result. With the Bayesian decision theory could statistically signi ¬Ã‚ cantly increase the accuracy. 6. Conclusion We conclude that the proposed approach for offline character recognition, which fits the input character image for the appropriate feature and classifier according to the input image quality. In existing system missing characters cant be identified. Our approach using Bayesian Decision Theories which can classify missing data effectively which decrease error in compare to hidden Markova model. Significantly increases in accuracy levels will found in our method for character recognition