Hana
A handwritten name database for offline handwritten text recognition
Bibliographic Data
| ID | 9814747 |
|---|---|
| Authors | Christian M Dahl (0000-0001-9880-2818, University of Southern Denmark, corresponding author), Torben S D Johansen (0000-0002-5964-5052, University of Southern Denmark), Emil N Sørensen (0000-0002-2492-7850, University of Bristol), Simon Wittrock (0000-0003-2879-575X, University of Southern Denmark) |
| Year | 2023 |
| Volume | 87 |
| Pages | 101473 |
| Publication date | 2023-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Explorations in Economic History (JOURNAL) |
| Journal identifiers | ISSN: 0014-4983 • E-ISSN: 1090-2457 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.eeh.2022.101473 |
| OpenAlex | W3199199880 |
| Language | EN |
| References cited | 8 |
Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Perhaps the single most important identifier for linking is personal names. However, personal names are prone to enumeration and transcription errors and although modern linking methods are designed to handle such challenges, these sources of errors are critical and should be minimized. For this purpose, improved transcription methods and large-scale databases are crucial components. This paper describes and provides documentation for HANA, a newly constructed large-scale database which consists of more than 3.3 million names. The database contains more than 105 thousand unique names with a total of more than 1.1 million images of personal names, which proves useful for transfer learning to other settings. We provide three examples hereof, obtaining significantly improved transcription accuracy on both Danish and US census data. In addition, we present benchmark results for deep learning models automatically transcribing the personal names from the scanned documents. Through making more challenging large-scale databases publicly available we hope to foster more sophisticated, accurate, and robust models for handwritten text recognition
Benchmark (surveying · Database · Documentation · Identifier · Information retrieval · Natural language processing · Transcription (linguistics · Artificial Intelligence · Computer Science · Handwritten Text Recognition Techniques · Natural Language Processing Techniques · Topic Modeling
How Well Do Automated Linking Methods Perform? Lessons from US Historical Data
Automated Linking of Historical Data
Europe's Tired, Poor, Huddled Masses
Have the poor always been less likely to migrate? Evidence from inheritance practices during the age of mass migration
Linking individuals across historical sources
Playing with matches
A Nation of Immigrants
Multiple Measures of Historical Intergenerational Mobility
| Citation velocity | historical |
|---|---|
| Highly cited | No |