English Handwritten Word Recognition

Abstract

Today, tons of data is being generated every day and this helps with the automation of several tasks. Automated recognition of handwritten words from images is one such challenging task. This can be done by extracting the important features out of an image. The major challenge for handwritten word recognition over optical word recognition is the inherent variation in the handwriting styles. To recognize such words there must be a model or a system. Thus, it is of utmost importance to build handwritten word recognition models with high accuracy. The model will face multiple challenges that need to be overcome to accurately predict the given word on its own. This model can be used in pharmaceuticals to convert the prescription or report images into scanned documents and store the relevant information from it. In this work, I will be building a deep-learningbased odel for the English Handwritten Dataset that can recognize the words from the images. Dataset used here is the IAM word dataset. This dataset is publicly available. CNN architecture helps to extract features from images. Features could be in the form of edges or blurred images. RNN helps to learn the model from the previous states and predict the output for the next state. This process is called sequential learning. Combining the strength of feature extraction from CNN and sequence learning from RNN i.e. C-RNN, I got 72.46% accuracy and 11.88% character error rate. Accuracy depends on the dataset used for training purposes.