Digitization of Handwritten text using Deep Learning
Sumita Gupta, Aditya Gupta, Simran Khanna, Shivam Arora · 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) · 2022
Handwriting recognition as challenging task for a machine with lot of variations in different aspects like tilt, contrast, strokes being different for every person even being different for the same person. This paper is assessing state of the art models for comparison and provides a simple deep learning model based on convolutional network and Connectionist Temporal Classification (CTC). The accuracy accomplished after implementation is not high up as state-of-the-art models but gives a general architecture to start from for handwritten text recognition (HTR). This work also provides a comparison between two stated decoding schemes based on CTC technique. The model in this paper has been trained on IAM dataset with “lines” images. The model gives a word error rate (WER) of 37% and character error rate of 14.8%. The model aims to predict the text of the image line at once instead of segmenting the lines into words