(Phonetic keyboard layout). Page 2. 2. Jaipur PC Key Assignments. (Devanagari Font). Straight keys (without pressing Shift, Alt or Control). ` 1 2 3 4 5 6. 7 8. 9. Hindi Inscript Keyboard Combination code - Inscript Shortcut Kyes, Some Character in Inscript Hindi Keyboard make using Combination of characters, See All. hindi typing keyboard Kruti Dev chart pdf download. Hindi Keyboard Layout For Kurti Dev and Delvys Font - Dark - Printable Version Hindi Font.
|Language:||English, Spanish, Dutch|
|Genre:||Fiction & Literature|
|Distribution:||Free* [*Registration needed]|
The layout of the various characters on a keyboard has pro- found impact on the better than the current Hindi keyboard in terms of typ- ing convenience and. A standard Hindi typing keyboard (Remington keyboard) layout with the hindi font Devlys and Kruti Dev. Remington Hindi keyboard is used in government typing. Hindi Code, Special Character Code for Hindi, Hindi typing code, The Code is combination of Alt key and some numeric value.
It is the most popular and fast way to learn hindi typing with remington keyboard layout. Its is keyboard layout use in type writter machine too. Here we are providng keyboard layout. How to install and enable Hindi Remington Gail Keyboard. See the layout below.
Inscript keyboard is the new keyboard designed for Indian Language Typing this format supports up to 12 indian language typing. It's not a popular keyboard for hindi typing and also more difficult to learn hindi typing with Inscript hindi keyboard.
Inscript keyboard also used in SSC typing test exam, ssc data entry test exam.
The steps for Enabling Inscript Hindi Keyboard are given in following link. Inscript keyboard Character Code Guide. Its the simplest method for Hindi Typing Without learning any keyboard lauout or practising.
The following characters were selected for the layout which represent the Nepali written language, as a subset of Devanagari Script. The statistical optimization problem can be solved with many optimum results using the Genetic Algorithms.
They are less susceptible to getting stuck at local optima than any other methods of optimizing keyboard layouts. Solving such kind of problems with the GA is an art of representing a solu- tions.
There may be many optimum layout satisfying the given layout. To use a genetic algorithm, the first and the most important part is to represent a solu- tion to the problem as a genome or chromosome. The genetic algorithm then creates a population of solutions and applies genetic operators such as mutation and crossover to evolve the solutions in order to find the best one s.
Since the algorithms selects only the best out of the population for the reproduction, the process ultimately lead to the best possible solutions. It may not be the best or the optimum but it by nature leads to the solutions near to the best ones. Further more it may not necessarily lead to the best ones. This report outlines some processes of the basics of genetic algorithms. The three most important aspects of using genetic algorithms are: 1. Once these three have been defined, the generic genetic algorithm should work fairly well.
Beyond that many different variations can be tried to improve performance, find multiple optima species - if they exist , or parallelize the algorithms. Offspring not necessarily but generally possess better quality than their parents. Hence the improvements are made from generation to generation. Genetic algorithm is an iterative process. It is not an exact mathematical method to solve a well defined problem. It is a heuristic approach to solve for an optimality in a huge domain of possible solutions.
As it is not an exact mathematical method it does not guarantee the best solution but according to the theory of the natural selection it ensures the solution near to the best ones.
The solution approaches best as number of generation increases. It also depends on other factors like the crossover probability, population size and mutation probability. Generally mutation probability is very low compared to the crossover probability. And the solution approaches its best faster as the population size is increased. Most difficult and prominent part of the implementation of genetic algorithm is the representation of the problem or the design of the chromosome genome.
Fitness function is the one which ensures the improvements in the chromosomes as the generation evolves. This is only the function which ensures the surviv- ability of the genomes.
So it must ensure all the constrains that must be took care of while optimizing the solution. And it depends on the language pattern too. The algorithm imple- mented and the fitness of the genome is computed by the weighted contribution of the individual six criteria coefficients.
And the final score of the keyboard is taken as the weighted sum of the six scores. Population produced in each one cycle is called a generation 6. Above process continued for a no. A concrete definition of the optimality of a keyboard is provided by . This definitionis motivated by the qualities listed above.
Their definition is discussed here and used as a standard metric in this work.
According to the method proposed, each keyboard is evaluated on six criteria and the final score of the keyboard is taken as a weighted sum of these six individual scores. These six criteria are: 1.
Load Distribution 2. Modifier Overhead 3.
Hand Alteration 4. Consecutive Usage of Same Finger 5. Big steps by fingers of same hand 6. Hit direction 8 Load Distribution Each finger of the hand has a certain strength. Note that while typing, the to- tal load on the fingers is constant. However,it would be highly desirable if this total load can be distributed among the fingers in proportion of their relative strengths. Therefore, the index finger which is the strongest should share most of the total load. Along similar lines,keys in the middle row are the most eas- ily accessible and therefore these should contain the most frequently occurring characters.
Hence, keys near the center of the keyboard which are hit by the index finger and in the middle row should share the maximum fraction of the total load. Formall ystated, we can assign an ideal load distribution betweenall the monographs and the performance of any keyboard can be measured by cal- culating the deviation that the actual load distribution for this keyboard has with the ideal distribution.
Hence, let fmi be the observed relative frequencyof desired a monograph mi having ideal relative frequency fm i let mi be the set of all monographs. To interpret the values the desired value for fmi can be obtained by multiplying the respective fractions given in table for row and column and then multiplying by a factor of so that the total load is divided equally between the left and right hands.
The computed value for the overall keyboard can be found at Appendix A. Row Column 1 Hence,the most commonly used characters should be assigned to normal macros while the lesser used characters must be assigned to shift modified macros. The performance of the keyboard on this index is measured by a factor v2 and is calculated by dividing the total number of keys pressed bythe number of characters that were typed.
Hence the modifier overhead will be 1. The reason is that this allows one hand to move to the nextkeys position while the other is in the process of hittingthe current key. In order to quantify this hand alternationindicator, we add up the frequency of the digraphs which aretyped by using one hand only. Consecutive Usage of Same Finger The same concept of hand alternation can be extended to fingers as well. If con- secutive keys are hit by the same finger,it might lead to in efficiency in typing.
This index is calculated by summing up the frequencies of all diagraphswhich require both keys to be hit by the same finger. However,instead of simply sum- ming up the frequencies of these diagraphs, these frequencies are first weighted by a distance coefficient.
The greater the distance between the two keysof a diagraph, the more penalizing a consecutive usage. The relevant set d4 there- fore the set of digraphs which are typed using the same finger. However instead of simply summing up the frequencies of these diagraphs, these frequencies are weighted first by a distance co-efficient. The greater the distance between the two keys of a diagraph, the more penalizing a consecutive usage.
The relevant set d4 therefore the sets of a diagraps which are typed using the same finger.