Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras

The identification of words and numerical characters in an offline setting falls within the realm of word processing and optical character recognition systems. The process of identifying appropriate techniques for extracting features and selecting a classifier can be a challenging undertaking. The present study outlines a methodology for the development of handwritten recognition systems utilizing a neural network classifier. The technique is being assessed through the examination of a dataset sourced from Kaggle, which pertains to the recognition of automobile number plates. The selection of frequently occurring words is performed manually from a repository of vehicles. These words then undergo several stages, including data acquisition, pre-processing, and feature extraction, to enable their recognition.