Handwritten Kannada character recognition using wavelet transform and structural features

Pasha, Saleem; Padma, M. C.

doi:10.1109/erect.2015.7499039

Cited by 24 publications

(3 citation statements)

References 4 publications

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“…The model shows classification accuracy of 91.00%. and 97.60% for Kannada handwritten characters and Kannada numerals respectively [8]. Single Kannada and English character recognition is performed based on zone features.…”

Section: Introductionmentioning

confidence: 99%

Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents

Bhargav, Shivayogi B. Ullagaddi

2023

tjjpt

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In the digital document analysis, handwritten character recognition is a challenging area. Various methods are proposed in the literature to identify the strike-outs in various languages. Kannada handwritten classification is a crucial machine vision problem due to its various practical applications in the development of recognition systems. Identifying of strikeout string on the given Kannada statement is an important challenge to develop robust recognition system. In the present study, pre-trained models such as DenseNet121, EficientNetB0, MobileNet, InceptionResNetV2 are used to recognize strikeout Kannada words. Experimental analysis indicates that EficientNetB0 performed better for strikeout Kannada words and InceptionResNetV2 results in higher performance for non-strikeout Kannada words.

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Section: Introductionmentioning

confidence: 99%

Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents

Bhargav, Shivayogi B. Ullagaddi

2023

tjjpt

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“…Indira and Selvi [20] reviewed various methods for Kannada printed character recognition. In another work applicability of wavelet transform and structural features are adapted for Kannada handwritten character recognition by Pasha and Padma [21]. The experimentations were conducted on hand written numerals.…”

Section: Introductionmentioning

confidence: 99%

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Rani

Manohar²,

Hariprasad³

et al. 2022

IJECE

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<p>Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize Kannada handwritten characters. Capsule network architecture is built of an input layer, two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer. For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.</p>

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“…They have achieved an accuracy of 90.39%, 93.17%, and 95.12%respectively. Pasha and Padma (2015) have proposed a handwritten Kannada character recognition system. They have used ANN using structural and wavelet transforms features.Theyhaveachievedanaccuracyof91%.…”

Section: Introductionmentioning

confidence: 99%

Online Handwritten Gujarati Word Recognition

Naik

Desai

2019

International Journal of Computer Vision and Image Processing

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In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.

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Handwritten Kannada character recognition using wavelet transform and structural features

Cited by 24 publications

References 4 publications

Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents

Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Online Handwritten Gujarati Word Recognition

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