![]() ![]() Many approaches to this problem divide the task into 3 separate components: segmentation, feature extraction, and classification. ![]() Misspellings, cross outs, and omissions are also common. cursive vs print vs block lettering), size, spacing, embellishments, and legibility. There are extreme variations between styles (e.g. Reading handwritten text is uniquely difficult. This post is a retrospective on that attempt as well as an explanation of design choices and training procedures. Towards that end, I endeavored to design a practical application which balances accuracy, generalizability, and inference speed. In light of advancements in computer vision and language processing, reliable and automated handwriting recognition is within reach. Prior to the deep learning revolution, no clear path existed towards achieving such a goal in a scalable way. Digitizing handwritten documents to improve storage, access, search, and analysis is a compelling challenge.
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