Handwritten Information Extraction from Historical Census Documents

This paper describes a complete system for hand-written information extraction in historical documents. The system was evaluated in real conditions and at a large scale (8 millions of snippets) on the tables of the 1930 US Census. The location of the table position was based on a registration algorithm using printed word anchors. The rows and columns were extracted for nine different fields. For each field, a recognizer based either on convolutional neural networks for small lexicon fields or recurrent neural networks for large lexicon fields were trained. This system yields very high results for data extraction, allowing to achieve more than 70% of automation rate at a error rate similar to human keyers for a complete identity field.