Handwriting text recognition

Computer engineering

This project seeks to classify an individual handwritten word so that handwritten text can be translated to a digital form. We used two main approaches to accomplish this task: classifying words directly and character segmentation. For the former, we use Convolutional Neural Network (CNN) with various architectures to train a model that can accurately classify words. For the latter, we use Long Short Term Memory networks (LSTM) with convolution to construct bounding boxes for each character. We then pass the segmented characters to a CNN for classification, and then reconstruct each word according to the results of classification and segmentation.

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