In codice ratio: OCR of handwritten Latin documents using deep convolutional networks

Automatic transcription of historical handwritten documents is a challenging research problem, requiring in general expensive transcriptions from expert paleographers. In Codice Ratio is designed to be an end-to-end architecture requiring instead limited labeling effort, whose aim is the automatic transcription of a portion of the Vatican Secret Archives (one of the largest historical libraries in the world). In this paper, we describe in particular the design of our OCR component for Latin characters. To this end, we first annotated a large corpus of Latin characters with a custom crowdsourcing platform. Leveraging over recent progresses in deep learning, we designed and trained a deep convolutional network achieving an overall accuracy of 96% over the entire dataset, which is one of the highest results reported in the literature so far. Our training data are publicly available.