Handwritten Text Recognition using Deep Learning

Handwritten Text Recognition using Deep LearningBatuhan AbstractThis project seeks to classify an individual handwrittenword so that Handwritten text can be translated to a digi-tal form. We used two main approaches to accomplish thistask: classifying words directly and character segmenta-tion. For the former, we use Convolutional Neural Network(CNN) with various architectures to train a model that canaccurately classify words. For the latter, we use Long ShortTerm Memory networks (LSTM) with convolution to con-struct bounding boxes for each character . We then pass thesegmented characters to a CNN for classification, and thenreconstruct each word according to the results of classifica-tion and IntroductionDespite the abundance of technological writing tools,many people still choose to take their notes traditionally:with pen and paper.

of a character being present. A CNN with two convolutional layers, two average pooling layers, and a fully connected layer was used to classify each character [11]. One of the most prominent papers for the task of hand-written text recognition is Scan, Attend, and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention [16].

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  Hands, Character, Written, Handwritten, Hand written

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