Optical character recognition for Telugu handwritten text using SqueezeNet convolutional neural networks model - Buddaraju Revathi; B N V Narasimha Raju; Ajay Dilip Kumar Marapatla; Kagitha Veeramanikanta; Katta Dinesh; Maddirala Supraja (Institute of Advanced Engineering and Science, 2024)
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%.