Printed Odia Symbols for Character Recognition: A Database Study

Line segmentation is a critical phase of the Optical Character Recognition (OCR) which separates the individual lines from the image documents. The accuracy rate of the OCR tool is directly proportional to the line segmentation accuracy followed by the word/character segmentation. In this context, an algorithm, named height_based_segmentation is proposed for the text line segmentation of printed Odia documents. The proposed algorithm finds the average height of a text line and it helps to minimize the overlapped text line cases. The algorithm also includes post-processing steps to combine the modifier zone with the base zone. The performance of the algorithm is evaluated through the ground truth and also by comparing it with the existing segmentation approaches.

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Performance Evaluation of Deep Learning Networks on Printed Odia Characters

Pattanayak¹,

Pradhan²,

Malik³

2020

Journal of Computer Science

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Deep machine learning includes a series of layers to mimic the working of the human brain for taking a decision. Deep learning networks have shown good results in character recognition in the past. This paper evaluates the performance of different deep learning networks like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) based recurrent neural network and Convolutional LSTM on printed Odia characters. The Odia character database contains more than 24,000 images of printed Odia characters (including simple as well as complex characters) out of which 23,857 nos. of images are chosen for this work. Besides these three, a nested Convolutional neural network model is developed for different categories of printed character image groups which are formed based on their writing style. Here, in this study, the nested model is showing the best results in terms of error rate, accuracy and no. of epochs in comparison to the other three. Different pre-processing steps like binarization, sizenormalization, blurring, interpolation, etc. are involved before passing the images to the deep neural networks to increase the recognition accuracy.

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Printed Odia Symbols for Character Recognition: A Database Study

Cited by 4 publications

References 8 publications

Quantitative Analysis of Deep CNNs for Multilingual Handwritten Digit Recognition

Quantitative Analysis of Deep CNNs for Multilingual Handwritten Digit Recognition

An Efficient Algorithm to Find the Height of a Text Line and Overcome Overlapped and Broken Line Problem during Segmentation

Performance Evaluation of Deep Learning Networks on Printed Odia Characters

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