Custom CNN for Gujarati Letter Recognition: Enhancing OCR Accuracy with Deep Learning

Detection as well as recognition of Gujarati letter (GL) forms is not only a crucial step in several projects, but also it can be considered to be an initial state of the languages processing pipeline. Optical Character Recognition (OCR) by Convolutional Neural Networks (CNN) has been seen to have promising outcomes which is the way by sending this method into automation. In this regard, we suggest a design of a custom CNN network comprising of the elements matching the local shape of the recognised GL. In the CNN model, a dataset of Gujarati characters is used as a domain for the hyperparameters to train, such as learning rate, batch size and dropout rate. The play through of the version under consideration is appraised using a classification an account, which will furnish information on the process, don't forget and F1-score for each magnificence. Last but not least, the metrics which encompass accuracy and loss is also computed to measure the general performance of the version being utilized. Through image processing automation that detects various types of Gujarati characters, this technology helps the process to save and evaluate the green data which is written in documents with Gujarati language as well. In addition, it enables the development of such factors as inclusive textbooks and tools for the community which is Gujarati-speaking. The proposed system of OCR device that exploits a specific CNN technique to resolve the demanding situations of Gujarati alphabet identification and classification shows how the deep learning algorithms could be useful in this process which can be invaluable both to language science and technological developments.