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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Revathi, Buddaraju
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Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Computationally efficient ResNet based Telugu handwritten text detection Revathi, Buddaraju; Prasad, M. V. D.; Gattim, Naveen Kishore
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8170

Abstract

Optical character recognition (OCR) is a technological process that converts diverse document formats into editable and searchable data. Recognition of Telugu characters through OCR poses a challenge because of compound characters. Identifying handwritten Telugu text proves difficult due to the substantial number of characters, their similarities, and overlapping forms. To handle overlapping characters, we implemented a segmentation algorithm that efficiently separates these characters, consequently enhancing the model’s accuracy. Feature extraction is a crucial phase in recognizing a broader range of characters, especially those that are similar in appearance. So, we have employed a light weighted ResNet 34 model that effectively addresses these challenges and handles deep networks without declining accuracy as the network’s depth increases. We have achieved a word level recognition rate of 81.5%. In addition, the parameters required by the model are less when compared to its counterpart inception V1, making it computationally efficient.
Efficient deep learning models for Telugu handwritten text recognition Revathi, Buddaraju; Raju, B. N. V. Narasimha; Rama Krishna, Boddu L. V. Siva; Kumar Marapatla, Ajay Dilip; Suryanarayanaraju, S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1564-1572

Abstract

Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.
Optical character recognition for Telugu handwritten text using SqueezeNet convolutional neural networks model Revathi, Buddaraju; Raju, B N V Narasimha; Marapatla, Ajay Dilip Kumar; Veeramanikanta, Kagitha; Dinesh, Katta; Supraja, Maddirala
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp487-496

Abstract

Optical character recognition (OCR) is a process that recognizes and converts data from scanned images, including both handwritten and printed documents, into an accessible format. The challenges in Telugu OCR arise from compound characters, an extensive character set, limited datasets, character similarities, and difficulties in segmenting overlapping characters. To tackle these segmentation complexities, an algorithm has been developed, prioritizing the preservation of essential features during character segmentation. For distinguishing between structurally similar characters, we used convolutional neural networks (CNN) due to their feature-extracting properties. We have employed the CNN model, the SqueezeNet for feature extraction, resulting in an impressive character recognition rate of 94% and a word recognition rate of 80%.
Co-Authors Dinesh, Katta Gattim, Naveen Kishore Kumar Marapatla, Ajay Dilip Marapatla, Ajay Dilip Kumar Prasad, M. V. D. Raju, B N V Narasimha Raju, B. N. V. Narasimha Rama Krishna, Boddu L. V. Siva Supraja, Maddirala Suryanarayanaraju, S. Veeramanikanta, Kagitha