From HMMs to RNNs: Computer-Assisted Transcription of a Handwritten Notarial Records Collection

We present the process which is being followed for the transcription of a large XVIII Century Manuscript collection with the help of Handwritten Text Recognition (HTR) Technology. The documents are being processed in batches of 50 pages each. For each batch we perform two semi-supervised processes: one in order to analyze the layout and detect the text lines and another to provide the full transcripts of the text. As per users request, both diplomatic and modernized transcripts, as well as semantically tagged versions are being produced. Layout analysis supervision is performed by means of a conventional layout editing tool. On the other hand, transcripts, including automatic modernization and tagging, are being produced by means of a web based computer-assisted interactive-predictive tool (CATTI). We present results of the performance of this process through 12 image batches processed so far. These results show the impact caused by an optical modelling technological transition: from classical HMM-based methods to new technology based on recurrent neural networks.