Online Handwriting Recognition of Tamil Script Using Fractal Geometry

Kunwar, Rituraj; Ramakrishnan, A. G.

doi:10.1109/icdar.2011.279

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…It is evident that N Â M multiplications for the calculation of a vector (17) and M multiplications for calculation of the conjugation index (18) are required. Thus, it is necessary to make the total of ðN þ 1ÞM multiplications.…”

Section: Orthogonal Representation and Computation Aspectmentioning

confidence: 99%

“…The compression IFS algorithm searches the best affine transformation from domain to range block for every range block. 18 As a result, an input image is coded by several affine transformations…”

Section: Localization and Fractal Compressionmentioning

confidence: 99%

“…17 In our research, we study the feature extraction approach based on fractal images. Fractal image recognition 18 is used in computer vision; for example, in 19 extracting intracellular fractal features, it has a good effect at the recognition stage.…”

Section: Introductionmentioning

confidence: 99%

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Support subspaces method for recognition of the synthetic aperture radar images using fractal compression

Fursov

Minaev

Zherdev

et al. 2017

International Journal of Advanced Robotic Systems

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The goal of this work is to develop a technology that can reduce recognition computational complexity with the rise of recognition quality. We use an approach based on implementation of the conjugation indices of the vectors with the class feature spaces. We suggest a new criterion of class separability based on the conjugation index and use it to form so-called support subspaces from the training vectors. This procedure decreases computing complexity at training stage about 1000 times in comparison with previous algorithm implementation and improves recognition quality. The most significant decrease of the computational complexity of the proposed technology is achieved by implementing the fractal compression to radar images. The results prove that using this technology leads to an increase of the recognition quality.

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Section: Orthogonal Representation and Computation Aspectmentioning

confidence: 99%

Section: Localization and Fractal Compressionmentioning

confidence: 99%

See 1 more Smart Citation

Support subspaces method for recognition of the synthetic aperture radar images using fractal compression

Fursov

Minaev

Zherdev

et al. 2017

International Journal of Advanced Robotic Systems

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show abstract

“…There have not been sufficient studies on TCR with machine learning models, but still, it is in the infant stage [15,35]. Among the countable researchers to recognize handwritten Tamil characters, the fuzzy concept is more primitive as it has the potential of string matching [30,41].…”

Section: Introductionmentioning

confidence: 99%

Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model

Lincy

Gayathri

2020

Multimed Tools Appl

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In recent data, Optical character recognition (OCR) systems have laid hands in the field of most popular language recognitions. Unlike other languages, the Tamil language is more complex to recognize, and hence considerable efforts have been laid in literature. However, the models are not yet well-organized for precise recognition of Tamil characters. Thus, the current research work develops a novel Tamil Handwritten Character Recognition approach by following two major processes, viz. pre-processing and recognition. The pre-processing phase encloses RGB to grayscale conversion, binarization with thresholding, image complementation, morphological operations, and linearization. Subsequently, the pre-processed image after linearization is subjected to recognition via an optimally configured Convolutional Neural Network (CNN). More particularly, the fully connected layer and weights are fine-tuned by a new Self Adaptive Lion Algorithm (SALA) that is the conceptual improvement of the standard Lion Algorithm (LA). The performance of the proposed work is compared and proved over other state-of-the-art models with respect to certain performance measures.

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“…More details of this database can be found in the lipitoolkit website [6]. This database is the de-facto standard among the research community for Tamil OCR and one can easily find numerous quality papers in the literature that used this database [25,26,27,28,29]. Very recently, Kavitha et al have used this database to test the efficacy of 9-layer CNN architecture and the proposed architecture achieved a training accuracy of 95.16% and test accuracy of 97.7% [25].…”

mentioning

confidence: 99%

uTHCD: A New Benchmarking for Tamil Handwritten OCR

Shaffi¹,

Hajamohideen²

2021

IEEE Access

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The robustness of a typical Handwritten character recognition system relies on the availability of comprehensive supervised data samples. There has been considerable work reported in the literature about creating the database for several Indic scripts, but the Tamil script has only one standardized database up to date. This paper presents the work done to create an exhaustive and extensive unconstrained Tamil Handwritten Character Database (uTHCD). The samples were generated from around 850 native Tamil volunteers including school-going kids, homemakers, university students, and faculty. The database consists of about 91000 samples with nearly 600 samples in each of 156 classes. This isolated character database is made publicly available as raw images and Hierarchical Data File (HDF) compressed file. The paper also presents several possible use cases of the proposed uTHCD database using Convolutional Neural Networks (CNN) to classify handwritten Tamil characters. Several experiments demonstrate that training on the proposed database helps traditional and contemporary classifiers perform on par or better than the existing dataset when tested with unseen data. With this database, we expect to set a new benchmark in Tamil handwritten character recognition and serve as a launchpad for developing robust language technologies for the Tamil script.

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Online Handwriting Recognition of Tamil Script Using Fractal Geometry

Cited by 12 publications

References 6 publications

Support subspaces method for recognition of the synthetic aperture radar images using fractal compression

Support subspaces method for recognition of the synthetic aperture radar images using fractal compression

Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model

uTHCD: A New Benchmarking for Tamil Handwritten OCR

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