Do Women Enjoy Equal Right in Nepal Essay Example for Free

Do Women Enjoy Equal Right in Nepal Essay Our country, Nepal is popularly known as a traditional nation. It gives more priority to its customs and traditions rather than other subjects of matter. This country has been ruled and operated by majority of men since the beginning of humanity. Men play the vital role in each sector of development. History of Nepal is the evidence that all the development works are carried out by majority of men and less women. Even though, there is equal importance of male and female in development of nation, females are kept aside in development as well as social matter. In each and every sector, men play a main role and women are in inferior position. The main cause of such vast differences is the lack of education and public awareness. Such differences start from the early period of males and females life. In point of view of Nepalese people, females are regarded as a curse and males as boon. They do not like bearing female baby as they have to get her married and provide dowry. So, they take it as burden. That’s why, when they bear a boy and a girl, they give higher priority and more love to boy and the girl is kept within the four walls of home. Thus, no matter how talented they are, they never get a chance to develop. The condition of women living in our country is really miserable. Women living in our society are still under the shadow of darkness. They are bounded by the traditional concept of conservative society. They are busy mostly in kitchen and household works. Though, women occupy more population in Nepal, they have less participation in the high level jobs and some other important matters related to their life. They can experience very few legal rights and even our society places them in an inferior position. This was often justified as being the result of biological differences between the sexes. Women were thought to be more emotional and less decisive than men. They are not given sufficient opportunities to improve themselves and are discouraged to go ahead. No matter whatever progress they make, they are still suppressed by this traditional society. Moreover, they are somehow  experiencing basic rights, but they are deprived of social rights in many ways. Furthermore, they are regarded as the symbol of creating, protecting and nursing. They bear and grow-up children. They have many more responsibilities like biological, social and national. But, still their condition in our nation is backward. They are so because of male domination, traditional, social structures, unequal laws, lack of awareness, poverty and lack of government protection for them. Women in our society have been confined only to household chores, rearing children, preparing food, collecting fodder for cattle and family sanitation. Especially, they do not have freedom for movement and for adopting job. They are deprived of higher studies and property rights. They are not encouraged for social exposure. Furthermore, they had been the victim of domestic violence from their husbands, brothers, mother-in-law and even relatives. Their appeal is not uprightly accepted even by the administrator. Sometimes, they are even beaten, kidnapped and killed. Hence, they are really living a sorrowful life. CBS (Central Bureau of Statistics) and the report of UNDP shows that women’s participation in developmental works of Nepal is of low grade. Their rate of involvement in different sectors like education, civil service, participation in teaching and legal practice is very low. Though many NGOs (National Government Organization), INGOs (International Government Organization) and governmental organizations are working for the rights of women, their condition has not been improved significantly. Therefore, in first place, the women themselves must raise voice for their rights. Government, stakeholders, NGOs and INGOs has to take certain measures to uplift the condition of women in every nook and cranny of Nepal. Awareness programs must be conducted in their favor. Rallies with slogans like â€Å"GENDER EQUALITY†, â€Å"EDUCATION FOR ALL†, and etcetera must be spread all over our country. Thus, women must be provided with rights equal to that of men. Reference; The present status of Nepali women Ashmita Bhattarai

Coffee Ulbs Essay Example for Free

Coffee Ulbs Essay Coffee is a brewed beverage with a distinct aroma and flavor from the roasted seeds of the coffea plant. Coffee comes in many types of colour such as dark brown,white,beige,black,light brown,and more. Coffee was first discovered in the northeast region of Ethopia. Cofee cultivation first took place in southern Arabia,appears in the middle of the 15th century in the Sufi shrines of Yemen. According to the ancient chronicle,Omar who was known for his ability to cure sick through prayer was once exiled from Mocha,Yemen to a desert cave near Ousab. Starving,Omar chewed berries from nearby shrubbery but found them to the bitter. He tried roasting the seeds to improve the flavor,but they become hard. He then tried boiling them to soften the seeds,which resulted in a fragrant brown liquid. Upon drinking the liquid,Omar was revitalized and sustained for days. As stories of this ‘miracle drug’ reached Mocha,Omar was asked to return and was made a saint. In production of coffee,it consist of many steps such as processing,roasting,grading the roasting seeds,decaffeination,stored,brewing and finally be served. When processing the coffee,the berries of coffee have been traditionally and selectively picked by hand,only the berries at the peak of ripeness would be selected. After that,green coffee is process by one of two methods. Whether by dry process method or wet process method. Then,it will be sorted by ripeness and colour. After that,the seeds are fermented to remove the slimy layer of mucilage still present on the seeds. When the fermentation is finished,the seeds are washed to remove the fermentation residue. Then,the seeds are dried. Finally,the coffee is sorted again and been labeled. The roasting process influences the taste of the beverage by changing the coffee seed both physically and chemically. During roasting,caramelization occurs as intense heat that breaks down starches,changing them to simple sugars that begin to brown,which alters the colour of seeds. Then the seeds will be grading depends on the colour of roasting seeds. It will be labeled as light,medium light,medium,medium dark,dark or very dark. The degree of roast has an effect upon coffee flavor and body. Many methods can remove the caffeine from coffee,but all involve either soaking the green seeds in hot water or steaming them and using a solvent to dissolve caffeine that containing oils. Once roasted,coffee seeds must be stored properly to preserve the fresh taste of the seeds. Coffee seeds must be ground and brewed to create a beverage. Almost all methods of preparing coffee require the seeds to be ground and mixed with hot water long enough to extract the flavor,but without overextraction that draws out bitter compounds. The roasted coffee may be ground at a roaster,in a grocery store or in the home. Then,the coffee may be brewed by several methods such as boiled,steeped,or pressurized. Once brewed,coffee may be served in a variety of ways. As an example,the white coffee was made into dairy product such as milk or cream or dairy substitute or as a black coffee with no such addition. It may be sweetened with sugar or artificial sweetener.

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

Edmund Spenser‘s Dazzling Quest for Virtue in The Faerie Queene Essays

Edmund Spenser‘s Dazzling Quest for Virtue in The Faerie Queene "Voyeur: one who habitually seeks sexual stimulation by visual means" (Webster's Ninth New Collegiate Dictionary). According to Baby's Record, as a child my favorite stories included Daniel in the Lions' Den, Jonah and the Whale, Elisha and the 40 Children Eaten by the Bears, The Three Little Pigs, and Goldilocks and the Three Bears. Before sex came violence, tamed by a mother's lap and blessed by the inspired Word. Voyeurism may well be "the relation . . . of every reader to every novel, of every spectator to every painting, play and film" (Paglia 191); as an "innocent" child, I had already allowed my "untamed pagan eye" to feast fully upon the delightful spectacle of human beings disappearing into the ravenous jaws of nature. To paraphrase Paglia I was at a tender age already "deeply implicated" (191). But perhaps sexuality has never been my strong suit. I must admit that I, unlike Paglia, saw The Faerie Queene above all as an allegory of "the Christian struggling heroically against many evils . . separated ...

Various law :: essays research papers

Charta Magna: agreement between king John and his barons laying down mutual rights and obligations as well as the position of the lower nobility and the church. (1215) Habeas Corpus: is an important remedy against unlawful commitment. (1679) Bill of rights: protects statements in either house of parliament granting parliament itself the power to fine or imprison those who abuse this privilege. It also prohibited the king to levy taxes or keep an army without permission of parliament. (1689) Act of settlement: Secured the succession of the throne after the death of William III who was king of England but who didn’t have any children. It gave the throne to Princess Sofia of Hannover and her heirs, being Protestants.(1700) Charles-Luis de Montesquieu : â€Å"De l’espiritu des lois† (1748) Jean-Jaques Rouseau is the author of: â€Å"discours sur l' origine el les fondaments de l' inegalite parmi les homes†(1754) â€Å"contrat social ou principes du droit† (1762) Independence of USA (1776) French Revolution (1784) Types of laws Statute laws: An act of the legislature of a state or country, declaring, commanding, or prohibiting something; a positive law; the written will of the legislature expressed with all the requisite forms of legislation; -- used in distinction from common law. Statute is commonly applied to the acts of a legislative body consisting of representatives. In monarchies, legislature laws of the sovereign are called edicts, decrees, ordinances, rescripts, etc. In works on international law and in the Roman law, the term is used as embracing all laws imposed by competent authority. Statutes in this sense are divided into statutes real, statutes personal, and statutes mixed; statutes real applying to immovables; statutes personal to movables; and statutes mixed to both classes of property. Statute book: a record of laws or legislative acts. Federal Laws: Rules that are applied on a federal level International Laws: A set of rules generally regarded and accepted as binding in relations between states and nations. Also called law of nations. These are the rules regulating the mutual intercourse of nations. International law is mainly the product of the conditions from time to time of international intercourse, being drawn from diplomatic discussion, textbooks, proof of usage, and from recitals in treaties. It is called public when treating of the relations of sovereign powers, and private when of the relations of persons of different nationalities. International law is now, by the better opinion, part of the common law of the land. By-laws: A local or subordinate law; a private law or regulation made by a corporation for its own government.

Movie Review: Fight Club Essay -- essays research papers

I Am Jack’s Paper The movie Fight Club shakes the foundations of our democratic nation, spits on our capitalist society, and makes all who watch it look at the American way of life differently. In a country driven by consumption, one can imagine the movie Fight Club rubs certain people the wrong way. When Edward Norton was asked why he decided to take the role as the main character in Fight Club, he replied, â€Å"to piss off America.† Each American since childhood has been told repeatedly that democracy equals freedom, but is this true? The only difference between capitalism and socialism is that corporations own everything in a capitalist society. In America â€Å"the things you own end up owning you.† Corporate America gives Americans a television in every home, a car in every driveway, and a Wal-Mart in every town. They call this freedom and freedom shall rain. This new breed of social democracy, an evolution of democracy where private enterprise controls Big Brother, is spreading through the world, infesting and exploiting every country and every government, from the sweatshops of Central America to the oilfields of Iraq; corporate America is slowly choking the world, one McDonalds at a time. Consumerism is the drive shaft of our generation, the fuel that pushes kids through college, and hope that one day we can have all the things seen in magazines and on TV. The dream of owning a house in the suburbs with a white picket fence and a SUV parked in the driveway. â€Å"Advertising h...

Cipd Level 3 4dep Avtivity 1 Essay

Activity 1 The CIPD HR Profession Map (HRPM) is a tool to assist HR practioners to assess what level they are working at â€Å"from band 1 at the start of an HR career through to band 4 for the most senior leaders† and to explore ways to develop their competencies to transition to the next level. The HRPM is divided into two groups – 10 professional areas and 8 behaviours – see the diagram below. The professional areas and behaviours are summarised below: Professional Areas| Strategies, Insights and Solutions| the HR practioner needs to have a deep understanding and insight of strategies and business activities. â€Å"This understanding – and resulting insights – allows us to create prioritised and situational HR strategies that make the most difference and build a compelling case for change†.| | Leading HR| describes how an HR professional must be able to lead themselves, others and activities to contribute to the overall business activities| | Service and delivery information| ensures that the delivery of HR services is accurate, timely and within budget| | Employee engagement| analyses the employee’s experience and creates opportunities for employee engagement which will have a beneficial effect on productivity, absenteeism, retention etc| | Employee relations| manages all relations with employees according to the organisations policies and procedures which are underpinned by relevant employment law | | Learning and talent development| aims to ensure that the workforce has the necessary skills to meet the short, medium and long term goals of the organisation| | Organisation design| ensures that the organisation is appropriately designed to deliver organisation objectives in the short and long-term and that structural change is effectively managed| | Organisation development| develops an organisation can develop its values and behaviours to match its culture and philosophy| | Performance and reward| ensure that reward packages, such as pension, bonuses etc maximise performance and retention within the workforce | | Resourcing and talent planning| provide a framework to recruit key people within legal parameters, and identify and develops high performers within an organisation | Behaviours| Curious| has an enquiring mind and looks for learning opportunities, both internally and  externally and who often asks for feedback on their performance| | Decisive thinker| is able to use pre vious experiences combined with current data and information, to make well measured discussions without always having to refer to a colleague or manager for advice| | Skilled influencer| influences across the organisation and its stakeholders â€Å"by using logical persuasion, backed by evidence to support their opinion or proposal†| | Personally credible| offers reliable and accurate advice and can remain impartial in sensitive situations| | Collaborative| works effectively on cross departmental and organisational projects, and readily shares knowledge and experiences to benefit the organisation| | Driven to deliver| consistently deliver objectives, within deadlines eg time and financial restraints| | Courage to challenge| Has the confidence to have courageous conversations when needed to challenge attitudes, behaviours and decisions| | Role model| recognises the values and behaviours of the organisation and encourages others to act within these standards| The activities and knowledge specified in the HR Profession Map for employee relations at Band 1 include: * â€Å"Monitor team performance against plans, recommending areas for improvement * Co-ordinate policy with others in the wider HR team, sharing ideas and best practices * Maintain relevant ER documentation, ensuring all contractual/legal documents are kept up to date and in line with current legislation * Implement guidelines on ER issues, ensuring that staff and managers receive updated policies and procedures * Inform and advise managers and staff about employee relations policies and practices * Give accurate and appropriate advice, training and support to managers who are managing difference and fair access to opportunities * Provide information to support managers who are resolving employee relation issues * Support managers in investigating and resolving employee relation issues, keeping accurate and appropriate records * Provide ad hoc reporting and trend analysis on ER issues * Manage and facilitate conflict situations to achieve consensus legally and ethically * Lead key negotiations with trade unions, works councils, employee forums, and so on, on a range of labour issues * Keep accurate records on health and safety compliance requirements† In my current role, employee relations plays a vital part of my roles and responsibilities,  particularly maintaining ER documentation to ensure compliance with employment law. Recently an ex-employee decided to challenge a decision about pay by means of an employment tribunal, so having the relevant ER documentation was imperative to defending the claim. Another key area is providing ad hoc and trend analysis on ER issues such as Bradford Factor reports to help Line Managers meet objectives for absence management targets. 788 words Bibliography Chartered Institute for Personnel and Development MEINTJES, R-S. (2010) Develop the Powers of Persuasion People Management ——————————————– [ 1 ]. CIPD HR Profession Map