Mail Processing with Deep Learning: A Case Study

Typed LettersHubert’s team decided early on it would be best for their deep learning space to deal with handwritten and typed letters differently..So they had to label letters in a provided training dataset as handwritten, typed, or both..They also needed to separate anything that was not a letter (envelopes, forms, etc.)..The team built a web platform and labeled about 2,000 documents manually into those four categories.From the set of labeled data, Hubert’s team needed to label the entire collection of unlabeled training data..They used autoencoders, which take an input like an image and ask a network to reproduce it.The network takes the input, puts it into a learning space, and recomposites it..The space is called latent space, and it contains the important features necessary to reconstruct an image quickly and efficiently.Hubert fed the 2,000 images to autoencoders and presented their manually determined labels to the latent space..This made it easy for a traditional machine learning algorithm to label all images in the dataset..The latent space-informed model achieved a 97 percent AUC performance and very low errors, meaning it very effectively recognized handwritten vs..typed letters.Deal with Typed LettersHubert said dealing with typed letters was the easiest part of creating the mail processing system..Using a tool called the Tesseract Open Source Optical Character Recognition Engine, the team simply inputted the images and specified their language..Tesseract outputted the fully digitized text.The Tesseract tool isn’t perfect..For instance, when it tries to parse signatures it produces wild and inaccurate characters.. More details

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