An optimized CNN system to recognize handwritten characters in ancient documents in Grantha script - Jindal, Amar; Ghosh, Rajib (International journal of information technology (Singapore. Online), 2023)
An optical character recognition (OCR) system plays an important role in the digitization of ancient handwritten text document. Various adversaries of ancient documents such as ink stains, faded portion of text, humidity spots, and similar-shaped characters make the task of character recognition challenging and tedious. This research study proposes an optimized convolution neural network (CNN) based OCR system to recognize each and every character present in the ancient document handwritten in Grantha script. A set of convolutional layers present in the proposed system extracts the deep hierarchical feature vectors from the input character image. Two fully connected neural network (FCNN) layers have classified these feature vectors into its correct class. The values of the hyper-parameters of CNN architecture such as the number of filters in each convolution layer, size of the filter in each convolution layer, number of FCNN layers, and neurons in each FCNN layer have been optimized using the Bayesian optimization technique. The major contribution of this work is the proposal of an optimized CNN architecture to perform OCR in ancient documents in Grantha script. A character recognition accuracy of 99.30% has been obtained from the proposed OCR system on the ancient handwritten documents in Grantha script. The experimental results demonstrate that the proposed OCR method outperforms the existing state-of-the-art methods in this regard.