CENSUS-HWR: a large training dataset for offline handwriting recognition
Chetan Joshi Lawry Sorenson Ammon Wolfert Dr. Mark Clement Dr. Joseph Price Dr. Kasey Buckles
Progress in Automated Handwriting Recognition has been hampered by the lack
of large training datasets. Nearly all research uses a set of small datasets
that often cause models to overfit. We present CENSUS-HWR, a new dataset
consisting of full English handwritten words in 1,812,014 gray scale images. A
total of 1,865,134 handwritten texts from a vocabulary of 10,711 words in the
English language are present in this collection. This dataset is intended to
serve handwriting models as a benchmark for deep learning algorithms. This huge
English handwriting recognition dataset has been extracted from the US 1930 and
1940 censuses taken by approximately 70,000 enumerators each year. The dataset
and the trained model with their weights are freely available to download at
https://censustree.org/data.html.