A hybrid decision tree for printed Tamil character recognition using SVMs

Ramanan, M.; Ramanan, Amirthalingam; Charles, E.Y.A.

doi:10.1109/icter.2015.7377685

Cited by 11 publications

(11 citation statements)

References 3 publications

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“…A convolutional neural network for proposing in offline handwritten Tamil characters was developed with a large number of datasets [1] it sets a benchmark for character recognition HTCR using the deep learning technique which includes the convolutional neural network for feature extraction, train the model. Using CNN as the model produces an accuracy of 95.16% in result analysis through the entire model description.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Various Deep learning Algorithms for Tamil Character Recognition

Kumar¹,

J²,

D³

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Process of converting Handwritten Tamil letters into editable form for extracting the information present in documents, Invoices that in handwritten letters that are difficult to find out the word present in the document because of bad handwriting and poor quality of the picture, Social media comments for analyzing and extracting the sentiment behind the text. Nowadays people started to deliver their thoughts through their own language Native Tamil. People find it easy to post thighs in Tamil which also leads to false information, violated messages, and cyberbullying. To address this, the notion of deep learning algorithms is implemented to classify the letters in digital form and also to suggest that the combination of letters obtained from deep learning results in better performance and good accuracy as well. The method involves the identification of optical characters to obtain feedback for the model and the creation of a model using neural networks which include CNN, Yolo and ResNet. Existing works involves using a simple OCR system but it results in a low-level recognition rate it finds difficult blurred images, shaken images, slightly tilted image's, poor quality images to recognize the text and give it back as an editable form to overcome this above situation selecting these models for testing with the Tamil character's to choose upon the best fit model to proceed for the future work of increasing the dataset and to recognize all the combination of words given in the input source of Tamil letters. The dataset used here is from HP-Labs India, which provides a large dataset of around 60,000 samples of isolated Tamil letters collected from both male and female candidates with different age groups. Tamil text classification is generally used to classify particular letters from handwritten letters in the digital form of images.

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Section: Related Workmentioning

confidence: 99%

Analysis of Various Deep learning Algorithms for Tamil Character Recognition

Kumar¹,

J²,

D³

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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“…Each handwritten signature image is enclosed in a tight fit rectangular boundary. The portion of the handwritten signature image outside this boundary is discarded using horizontal and vertical projection technique [12].…”

Section: Boundary Extractionmentioning

confidence: 99%

Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

Sriwathsan

Ramanan

Weerasinghe

2020

ARJOM

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Biometric recognition for human identification plays a key role in the rapid development of computer vision and pattern recognition research areas. The biometrics, refers to the automatic identification of a person based behavioral characteristics, physiological properties or traits. Signature recognition is one such human identification method and can be performed either in offline or online mode. This paper proposed an offline handwritten signature recognition which is based on image processing technique, scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) features and support vector machines (SVMs). The handwritten signature images were then recognized through the proposed method that involves identification of regions of interest and representation of those regions as SIFT or SURF, construction of codebooks and the multi-class classification of the feature histograms using support vector machines (SVMs). Experiments have been carried out with our dataset of 1600 samples and a recognition rate in excess of 95% was obtained over the ten-fold cross validations.

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“…Ramanan et al [7] introduced a new procedure to identify Tamil characters using binary Support Vector Machine (SVM) toward multiclass classification with Decision Tree. Here Binary rooted Directed Acyclic Graph (DAG) decision was practiced for Unbalanced Decision Trees (UDT).…”

Section: Related Workmentioning

confidence: 99%

“…So that there can be 4 groupings; 11, 10, 01 and 00. This can be written as follows, P(T=1).P(T=1)+P(T=1).P(T=0)+P(T=0).P(T=1)+P(T=0).P( T=0)=1 P(T=1).P(T=0)+P(T=0).P(T=1)= 1-P 2 (T=0) -P 2 (T=1) It can be rewritten as (7) where G is the Gini index, T is the Target variable and P is the proportion of observation of the target variable. Similarly, for the categorical target variable, the Gini index will be similar only with slight modification as follows,…”

Section: A Gini Indexmentioning

confidence: 99%

Systematic Classification of Historical Handwritten Tamil Palm Leaf Manuscript using CART algorithm and RBF Network

Sornam*¹,

M²

2019

IJITEE

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Character classification in the handwritten Tamil palm-leaf manuscript is more challenging than the other document character classification due to degradation and ancient characters in the palm-leaf manuscript. In this work, RBF (Radial Basis Function) network and CART (Classification and Regression Tree) were used to classify the Tamil palm leaf segmented characters. This work consists of two phases: In the first phase, the scanned Tamil palm leaf images were preprocessed by converting them into a grayscale image and then the images were allowed to remove noise using a median filter. In the second phase, GLCM (Gray Level Co-occurrence Matrix) feature extraction method was used to extract the statistical features from the segmented characters and these features were used to train the RBF network and CART algorithm. For the RBF network, Nguyen-Widrow weight initialization technique was used to generate the weight instead of random initialization. The dataset used in this work is Kuzhanthai Pini Maruthuvam (Medicine for child-related disease). By comparing RBF using Nguyen-Widrow method with CART algorithm, RBF yields promising result of 98.4% of accuracy whereas CART produced 98.8% of accuracy for character classification. The digitization of the Tamil palm-leaf manuscript will preserve the historical secrets, traditional medicine to cure disease, healthy lifestyle, etc. It can be used in the archeological department and Tamil libraries having a palm leaf script to preserve the manuscript from degrading.

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A hybrid decision tree for printed Tamil character recognition using SVMs

Cited by 11 publications

References 3 publications

Analysis of Various Deep learning Algorithms for Tamil Character Recognition

Analysis of Various Deep learning Algorithms for Tamil Character Recognition

Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

Systematic Classification of Historical Handwritten Tamil Palm Leaf Manuscript using CART algorithm and RBF Network

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