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. (Institute of Advanced Engineering and Science, 2024)
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.