Trygve Andersen
Biographic Data
| ID | 9450995 |
|---|---|
| NAME | Trygve Andersen |
| GIVEN NAMES | Trygve |
| FAMILY NAME | Andersen |
| SIGNATURE | ANDERSEN T |
| AFFILIATIONS | UiT The Arctic University of Norway |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes
Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end pipeline that scales to the dataset size and a model that achieves high accuracy with few manual transcriptions. The correctness of the model results must also be verified. This paper describes our lessons …
A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demog…
No prominent works on this page.
A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demog…
Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes
Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end pipeline that scales to the dataset size and a model that achieves high accuracy with few manual transcriptions. The correctness of the model results must also be verified. This paper describes our lessons …
Artificial Intelligence (2 works) · Computer Science (2 works) · Handwritten Text Recognition Techniques (2 works) · Image Processing and 3D Reconstruction (2 works) · Census (1 works) · Correctness (1 works) · Handwriting (1 works) · License (1 works) · Linguistics (1 works) · Machine learning (1 works)