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Torben Johansen

Publications and source records attributed to Torben Johansen.

2 recordsLinked to original sources
arXiv2024

Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINE

This paper introduces OccCANINE, an open-source tool that maps occupational descriptions to HISCO codes. Manual coding is slow and error-prone; OccCANINE replaces weeks of work with results in minutes. We fine-tune CANINE on 15.8 million description-code pairs from 29 sources in 13 languages. The model achieves 96 percent accuracy, precision, and recall. We also show that the approach generalizes to three systems - OCC1950, OCCICEM, and ISCO-68 - and release them open source. By breaking the "HISCO barrier," OccCANINE democratizes access to high-quality occupational coding, enabling broader research in economics, economic history, and related disciplines.

cs.CL↗
arXiv2021

HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition

Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Probably 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 contain 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.

cs.CV↗