Handwritten text recognition using cnn

Computer engineering

Variety of important things are there that we all have in common. However, several distinctions mark the identity of each individual. Apart from DNA, fingerprints, and other biometrics, another distinctive feature which lately, fresh assessments on handwriting evaluation have released is handwriting. Although duplication of handwriting is debatable and fabrication is a big problem, several factors like pen holding method, pressure applied, type of strokes, etc, give uniqueness to handwritten textThis research paper discusses the use of Convolutional Neural Networks (CNN) for Handwritten Text Recognition (HTR) tasks. HTR is the detection of characters from images. HTR is a complex task due to the variability and diversity of handwritten characters in the script. CNNs are a type of deep learning algorithm that can automatically learn features from images and are widely used in image recognition tasks. This paper presents a CNN-based approach for HTR that achieves state-of-theatre performance on a benchmark dataset. The proposed approach involves a pre-processing step to normalize and segment the input images, followed by a CNN architecture that consists of several convolutional layers and fully connected layers. The network is trained using a massive character-labelled dataset. The outcomes demonstrate that the suggested method achieves excellent accuracy in recognizing characters and can be applied to real-world applications such as document digitization and text-to-speech conversion.

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