Gamma Enhanced Binarization - An Adaptive Nonlinear Enhancement of Degraded Word Images for Improved Recognition of Split Characters

Kumar, Himanshu; Ramakrishnan, A. G.

doi:10.1109/ncc.2019.8732254

Cited by 3 publications

(2 citation statements)

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“…Kumar and Ramakrishnan [15] proposed an elegant method to recognize merged and split characters occurring in degraded printed documents. Their approach is a non-linear enhancement on histogram of the gray level degraded word images using power-law transformation (PLT) where fractional values of gamma are chosen automatically by their algorithm.…”

Section: Introductionmentioning

confidence: 99%

Degraded character recognition from old Kannada documents

Narasimhaiah

Rangarajan

2022

IJECE

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This paper addresses preparation of a dataset of Kannada characters which are degraded and robust recognition of such characters. The proposed recognition algorithm extracts the histogram of oriented gradients (HOG) features of block sizes 4x4 and 8x8 followed by principal component analysis (PCA) feature reduction. Various classifiers are experimented with and fine K-nearest neighbor classifier performs best. The performance of proposed model is evaluated using 5-fold cross validation method and receiver operating characteristic curve. The dataset devised is of size 10440 characters having 156 classes (distinct characters). These characters are from 75 pages of not well preserved old books. A comparison of proposed model with other features like Haar wavelet and Geometrical features suggests that proposed model is superior. It is observed that the PCA reduced features followed by fine K-nearest neighbor classifier resulted in the best accuracy with acceptance rate of 98.6% and 97.9% for block sizes of 4x4 and 8x8 respectively. The experimental results show that HOG feature extraction has a high recognition rate and the system is robust even with extensively degraded characters.

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Section: Introductionmentioning

confidence: 99%

Degraded character recognition from old Kannada documents

Narasimhaiah

Rangarajan

2022

IJECE

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“…An adaptive thresholding technique has been applied by H. R. Shiva Kumar, and A. G. Ramakrishnan [18]on gray scale word images for nonlinear enhancement.The word images are collected across ancient documents to connect the broken strokes of characters. Experiments are conducted over 1685 degraded sample images collected from ancient Kannada documents.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Degraded Printed Kannada Character Recognition

T.N¹,

LalithaRangarajan²

2021

INDJCSE

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Recognition of degraded printed Kannada characters is a challenging research problem. Proposed in this paper is a deep convolutional neural network for recognition of degraded printed Kannada characters. Characters in some old Kannada texts are affected by various degradations that result in breakages and dilations of characters introducing challenges in the process of recognition. The architecture consists of three levels, the first two levels with ReLu, Max pooling layers, and the third level with just ReLu. The output of these is input to fully connected layer which performs classification of characters. Experimental analysis is carried out using 156 classes of characters each class with 100 instances. Performance is evaluated for 4 epochs with 60 iterations per epoch. Highest classification accuracy of 99.51% has been reported for 75% training.

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