Introduction Not long ago, computer systems were like separate worlds, isolated from one another.
This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers.
The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm.
Introduction The need to recognize the handwritten text is challenging problem not only from the perspective of behavioural biometrics but also in the context of pattern recognition.
Writing is the most natural mode of collecting, storing, and transmitting the information. It is a widely used communication tool among human being and forms the input for simulation of reading by a machine.
The intensive research effort in the field of character recognition CR was due to challenges on simulation of human reading and also because of its potential applications, for example, postal automation, bank cheque analysis and processing, conversion of handwritten text into Braille, hand drawn pictogram or formula recognition, and so forth.
Pattern recognition is a computationally intensive and time-consuming task due to vast amount of image data and large number of computational steps involved.
The great demand for fast classification of letters by the post office requires a fast automated recognition system. Traditionally, the conventional approach always demands a very high speed computer or a parallel computer system to perform a satisfactory and fast recognition.
We cannot meet these demands using simple digital computer. Digital computers are good at handling problems which are explicitly formulated, but handwritten character recognition is not such a problem.
With the advent of neurocomputing technology, the great research effort has been devoted to formulate the pattern recognition tasks in an efficient manner.
Present study investigates the direction for the improvement of performance in Devanagari CR system.
There are 18 official languages accepted in the present Indian constitution. Twelve different scripts are used for writing these languages. Many of the Indian documents are supposed to be written in three languages, namely, English, Hindi, and the state official language as per the three language formula [ 1 ].
Hindi is the popularly used language of India and is the third most popular language in the world, which is written and encoded using Devanagari script. Not only Hindi but also the other languages such as Marathi, Sanskrit, and Konkani are encoded into Devanagari script.
Basic characters in Devanagri script consists of 13 vowels and 36 consonants [ 2 ] as shown in Figure 1. Writing style is from left to write. There is no concept of upper and lower case.
Vowels following consonants take a modified shape and are known as modified characters. Shape of modified characters varies depending on whether the vowel modifier is placed to the left, right, top, or bottom of the consonants as shown in Table 1.
In Devanagari script, there is a practice of using more than twelve different forms each of 36 consonants [ 3 ], giving rise to its shape variation.
The existence of modifiers and the compound characters, shown in Figure 2makes the character recognition more difficult for Devanagari script. Modifiers in Devanagari script. Basic characters of Devanagari script.
Compound characters of Devanagari script. A key reason for the absence of sustained research efforts in Devanagari Optical Character Recognition OCR is mainly because of the paucity of data resources.
Ground-truthed data for words and characters, on-line dictionaries, corpora of text documents, reliable standardized statistical analysis, and evaluation tools are currently lacking. So, the creation of such data resources will undoubtedly provide a much needed fillip to researchers working on Devanagari OCR.
Fuzzy model based recognition scheme was proposed by Hanmandlu and Murthy [ 4 ] for isolated Devanagari numerals.Optical character recognition (also optical character reader, OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text .
Methods and systems for recognizing Devanagari script handwriting are provided. A method may include receiving a handwritten input and determining that the handwritten input comprises a shirorekha stroke based on one or more shirorekha detection criteria.
The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals.
identification, character recognition, handwriting analysis, electrocardiographic signal analysis/understanding, medical diagnosis. In India, people speak hundreds of languages. The first research paper on handwritten devanagari character was . LANGUAGES WORLD WIDE: LANGUAGES H-R (Text, Images, Videos/Movies & Audio/Sound).
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|UTS # Unicode Locale Data Markup Language||Key ingredients are WARMreader for the route labeling and babel for the place names.|
|Zarma language of the Songhay family.|