Online handwritten malayalam character recognition using LIBSVM in matlab

Joseph, Steffy Maria; Hameed, Abdul Zubar

doi:10.1109/nccsn.2014.7001151

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…Coordinate vectors have been useful for identifying different hand-written styles with different inclinations, as shown in the current experimentation. This aligns with previous finding for the Malayalam language ( Joseph & Hameed, 2014 ), although in this work they mainly tested their approach with straight handwriting rather using texts with different inclinations. Thus, the proposed approach has shown the utility of coordinate vectors for handwriting recognition in texts with different inclinations.…”

Section: Discussionsupporting

confidence: 86%

Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB

Saloum

García‐Magariño

2021

PeerJ Computer Science

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Hand-written text recognition is useful for interpreting records in different fields such as healthcare, surgery and police in which professionals may avoid technical equipment and prefer writing notes on paper. In order to perform data fusion from different data sources, handwriting automatic recognition involves barriers such as different ways of writing letters and deformation due to many reasons. This work presents a novel handwriting recognition approach based on the application of coordinate vectors to find similarities in different kinds of deformations. In particular, it has been implemented using 16 segments in order to distinguish all the particularities in matching the new text considering a dataset with a machine-learning approach. The implementation of this approach with MATLAB shows promising results with accuracy of 92.8% for with ensemble and bagged trees, after analyzing 22 possible combinations of machine learning and processing techniques.

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Section: Discussionsupporting

confidence: 86%

Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB

Saloum

García‐Magariño

2021

PeerJ Computer Science

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“…Classification is an essential phase in the recognition process. Classifiers map the feature vector that represents a character in one of the possible classes.SVM is a popular classification technique which is found to be effective in OHCR for Malayalam [9] SVM is a supervised learning technique where training information with an accurate specification of different classes pertains to train a novel model. This novel model is utilized to test knowledge for appropriate categorization.…”

Section: Classification Experiments Using Support Vector Machinementioning

confidence: 99%

Segmentation of Malayalam Handwritten Characters into Pattern Primitives and Recognition using SVM

Baiju.K.B

Sabna.T.S

Lajish.V.L

2020

IJEAT

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This paper describes a lexical analysis (segmentation) approach in Pattern Recognition for Online Handwritten Character Recognition (OHCR) in Malayalam. The subunits (Pattern Primitives) in the single stroke vowel characters in Malayalam are identified and marked with pattern primitives to obtain a reference set of characters. Segmentation of the handwritten character samples into pattern primitives is made using a Combined Approach of Ramer Douglas Peucker algorithm and Eight Direction Freeman Code as per reference set. Features that are unique in the primitives of a character are extracted. The discriminating features identified are the direction of first primitive, segment count, cusp in second primitive, crossing in third primitive, and cusp in seventh primitive. The experiments were conducted on 100 samples per character that showed exact segmentation as per the reference set. With a five dimension feature set, the study achieved a recognition rate of 95.77% for five-fold cross-validation using Support Vector Machine with RBF kernel. The study shows that the segmentation of characters into pattern primitives is an effective method to realize accurate Malayalam OHCR systems for real-time applications.

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Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras

Hashmi,

2023

2023 3rd International Conference on Advancement in Electronics &Amp; Communication Engineering (AECE)

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Online handwritten malayalam character recognition using LIBSVM in matlab

Cited by 8 publications

References 6 publications

Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB

Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB

Segmentation of Malayalam Handwritten Characters into Pattern Primitives and Recognition using SVM

Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras

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