scholarly journals Automatic Telugu Handwritten Character Recognition with the Help of Legendre-Sobolev Divergent Machine Learning Model

2019 ◽  
Vol 8 (2) ◽  
pp. 2283-2288
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Machine Design ◽  
Wide Scope ◽  
Indian Language ◽  
Machine Learning Model ◽  
Online Handwriting Recognition ◽  
Online Handwriting

Online handwriting recognition or character recognition is the process in which a handwritten message is recognized by processing the handwritten data. It is the way toward changing over manually written characters to machine design. In penmanship, the strokes are made out of two arrange follows in the middle of pen down and pen up marks. Wide scope of highlights is extricated to play out thse acknowledgment. A complete internet hand-written recognition system for Indian language such as Telugu that addresses the ambiguities in separation just as recognition of buttons the recognition relies on conceptual model of penmanship structure joined with either a prejudicial model for stroke command. Such a methodology be able to flawlessly incorporate language and content data in the reproductive model then manage comparative and non-comparable strokes utilizing the single discriminative stroke grouping model. In this examination, we are utilizing disparate Legendre Sobolev conditions with the assistance of AI model, to such an extent that accomplishes 99.65% precision and improved the condition of craftsmanship esteem.

Online Tibetan Handwriting Recognition for Large Character Set on New Databases

2019 ◽  
Vol 33 (10) ◽  
pp. 1953003
Author(s):  
Weilan Wang ◽  
Zhengjiang Li ◽  
Zhengqi Cai ◽  
Xiaobao Lv ◽  
Caike Zhaxi ◽  
...  
Keyword(s):  
Information Technology ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Sample Number ◽  
Online Handwriting Recognition ◽  
Different Types ◽  
Character Set ◽  
Character Feature ◽  
Recognition Software ◽  
Online Handwriting

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is [Formula: see text]. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.

scholarly journals KANNADA CHARACTER RECOGNITION SYSTEM USING NEURAL NETWORK

2012 ◽  
pp. 206-208
Author(s):  
SURESH KUMAR D S ◽  
AJAY KUMAR B R ◽  
K SRINIVASA KALYAN
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Indian Language ◽  
Research Areas ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form[1]. As there are no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized into 20x30 pixel.The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

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