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Cambridge Digital Humanities

Cambridge Digital Humanities course timetable

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Wed 10 Jun – Wed 5 Aug

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June 2020

Wed 10
Introduction to Archival Photography workshop [cancelled re Covid-19] new CANCELLED 11:00 - 12:30 Cambridge Digital Humanities Online

We are currently reformatting our Learning programme for remote teaching; this will require some rescheduling so bookings will reopen and new sessions will be created for online courses as soon as possible. In the interim we would encourage you to register your interest so as to be notified of the new schedule. Please be aware that we hope to run many of our courses online, but that this is dependent on staff availability and resources so please be aware we may have to postpone or cancel some sessions

This session focusses on providing photography skills for those undertaking archival research. Dr Oliver Dunn has experience spanning more than 10 years digitising written and printed historical sources for major university research projects in the humanities and social sciences. The focus is very much on low-tech approaches and small budgets. We’ll consider best uses of smartphones, digital cameras and tripods.

Thu 11
(Critical) Machine Vision for the Humanities [remote delivery] new (2 of 10) Finished 15:00 - 15:45 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Mon 15
Machine Reading the Archive 2020 - end of programme workshop [see eventbrite] new Finished 11:30 - 15:30 Cambridge Digital Humanities Online

We are currently reformatting our Learning programme for remote teaching; this will require some rescheduling so bookings will reopen and new sessions will be created for online courses as soon as possible. In the interim we would encourage you to register your interest so as to be notified of the new schedule. Please be aware that we hope to run many of our courses online, but that this is dependent on staff availability and resources so please be aware we may have to postpone or cancel some sessions

This public workshop will mark the end of the 2020 programme of Machine Reading the Archive, a digital methods development programme organised by Cambridge Digital Humanities with the support of the Researcher Development Fund.

It will showcase the digital archive projects created by our cohort of project participants as well as invited contributions from leading experts in the field.

Tue 16
Bug Hunt 2020 [cancelled - Covid 19] new (5 of 5) CANCELLED 13:00 - 15:00 Cambridge University Library, IT Training Room

This programme is an opportunity to learn, through practical experience and shared investigation, how to apply digital methods for exploring and analysing a body of archival texts. The core of the programme will be 5 x 2 hour classroom based sessions supplemented by group and individual work on tasks related to the project design, delivery and documentation in between sessions. In addition to attending all five face-to-face sessions, participants should set aside an additional 8-10 hours over the duration of the course for work on project-related tasks.

During the programme we’ll work together on a particular topic: how insects were represented in books created for children in the 19th century. This question will help us to think about how children’s encounters with the natural world might have been framed and shaped by their reading. We’ll work on digital collections of 19th century children’s books exploring how such collections are built and how they can be used for machine reading. We’ll develop specific research questions and you’ll learn how to explore them using different tools for textual stylistic analysis. At the end, we’ll present findings and consider the implications of what we’ve discovered.

Topics covered include;

• The development of methods for machine reading the archive – ideas, motivations and ethics • Children’s books of the long 19th century – a beginner’s guide • Designing a small-scale investigation • Building a collection of digital texts • Transforming texts into searchable data • Analysing stylistic patterns in the data

(Critical) Machine Vision for the Humanities [remote delivery] new (3 of 10) Finished 15:00 - 16:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Wed 17
Game Design: an introduction for researchers [remote delivery] new (1 of 4) Finished 16:00 - 16:45 Cambridge Digital Humanities Online

Emma Reay is a third-year PhD researcher at the University of Cambridge and an associate lecturer at Anglia Ruskin University. Her current project explores depictions of children in videogames, and her research interests include representation studies, children's digital media, gaming and education, and playful activism.

Adam Dixon is a game designer and writer who makes both physical and digital games. He has worked on everything from big public games that involve running around cities to narrative video games about learning scientific skills. Much of his work has involved working with museums and research organisations such as the Wellcome Trust, Science Museum, Nottingham Trent University and the V&A. This has included designing games, using play for public research engagement and most recently, teaching teenagers to create digital games for Wellcome Collection’s Play Well exhibition. Outside of that he works and releases his own games including roleplaying games, LARPs and interactive fiction.

Applications https://www.cdh.cam.ac.uk/file/cdhgamedesign201920applicationdocx-0 should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Wednesday 10 June 2020. Successful applicants will be notified by 15 June 2020.

This online course will introduce participants to the practice of game design. It will explore the different ways that digital and analogue games are designed, particularly how you can design with intent to communicate a mood, theme or message. Participants will learn game design skills - such as boxing-in, design documents and prototyping – alongside opportunities to test them out by creating their own short games. Examples will focus on game design in research-related contexts, including using games as part of your research process and to communicate research outcomes to diverse audiences.

The sessions focus on game design, how to shape mechanics and play experiences, so no technical skills are needed. Participants will create their short games using both non-digital tools and simple, free software that will be taught in the sessions.

Topics covered:

  • Game design basics
  • A chance to play and consider thoughtful games
  • Boxing in
  • Planning games
  • Making games
  • Bitsy and Twine
  • Playtesting and iteration

Format

The course will be delivered online, with live teaching sessions taking place on Zoom.

  • Weds 17 June, 4pm BST: Introduction (45 minutes)
  • Weds 24 June, 4pm BST: Game play feedback (45 minutes)
  • Weds 1 July, 4pm BST: Game design seminar (45 minutes)
  • Weds 15 July, 4pm BST: Final session (60 minutes with break)

A CRASSH blog post was created for the originally scheduled session which may be of interest to read and can be found here: http://www.crassh.cam.ac.uk/blog/post/Play-as-Research-Practice

Thu 18
(Critical) Machine Vision for the Humanities [remote delivery] new (4 of 10) Finished 15:00 - 16:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Tue 23
(Critical) Machine Vision for the Humanities [remote delivery] new (5 of 10) Finished 15:00 - 16:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Wed 24
Game Design: an introduction for researchers [remote delivery] new (2 of 4) Finished 16:00 - 16:45 Cambridge Digital Humanities Online

Emma Reay is a third-year PhD researcher at the University of Cambridge and an associate lecturer at Anglia Ruskin University. Her current project explores depictions of children in videogames, and her research interests include representation studies, children's digital media, gaming and education, and playful activism.

Adam Dixon is a game designer and writer who makes both physical and digital games. He has worked on everything from big public games that involve running around cities to narrative video games about learning scientific skills. Much of his work has involved working with museums and research organisations such as the Wellcome Trust, Science Museum, Nottingham Trent University and the V&A. This has included designing games, using play for public research engagement and most recently, teaching teenagers to create digital games for Wellcome Collection’s Play Well exhibition. Outside of that he works and releases his own games including roleplaying games, LARPs and interactive fiction.

Applications https://www.cdh.cam.ac.uk/file/cdhgamedesign201920applicationdocx-0 should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Wednesday 10 June 2020. Successful applicants will be notified by 15 June 2020.

This online course will introduce participants to the practice of game design. It will explore the different ways that digital and analogue games are designed, particularly how you can design with intent to communicate a mood, theme or message. Participants will learn game design skills - such as boxing-in, design documents and prototyping – alongside opportunities to test them out by creating their own short games. Examples will focus on game design in research-related contexts, including using games as part of your research process and to communicate research outcomes to diverse audiences.

The sessions focus on game design, how to shape mechanics and play experiences, so no technical skills are needed. Participants will create their short games using both non-digital tools and simple, free software that will be taught in the sessions.

Topics covered:

  • Game design basics
  • A chance to play and consider thoughtful games
  • Boxing in
  • Planning games
  • Making games
  • Bitsy and Twine
  • Playtesting and iteration

Format

The course will be delivered online, with live teaching sessions taking place on Zoom.

  • Weds 17 June, 4pm BST: Introduction (45 minutes)
  • Weds 24 June, 4pm BST: Game play feedback (45 minutes)
  • Weds 1 July, 4pm BST: Game design seminar (45 minutes)
  • Weds 15 July, 4pm BST: Final session (60 minutes with break)

A CRASSH blog post was created for the originally scheduled session which may be of interest to read and can be found here: http://www.crassh.cam.ac.uk/blog/post/Play-as-Research-Practice

Thu 25
(Critical) Machine Vision for the Humanities [remote delivery] new (6 of 10) Finished 15:00 - 15:45 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Tue 30
(Critical) Machine Vision for the Humanities [remote delivery] new (7 of 10) Finished 15:00 - 16:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions

July 2020

Wed 1
Game Design: an introduction for researchers [remote delivery] new (3 of 4) Finished 16:00 - 16:45 Cambridge Digital Humanities Online

Emma Reay is a third-year PhD researcher at the University of Cambridge and an associate lecturer at Anglia Ruskin University. Her current project explores depictions of children in videogames, and her research interests include representation studies, children's digital media, gaming and education, and playful activism.

Adam Dixon is a game designer and writer who makes both physical and digital games. He has worked on everything from big public games that involve running around cities to narrative video games about learning scientific skills. Much of his work has involved working with museums and research organisations such as the Wellcome Trust, Science Museum, Nottingham Trent University and the V&A. This has included designing games, using play for public research engagement and most recently, teaching teenagers to create digital games for Wellcome Collection’s Play Well exhibition. Outside of that he works and releases his own games including roleplaying games, LARPs and interactive fiction.

Applications https://www.cdh.cam.ac.uk/file/cdhgamedesign201920applicationdocx-0 should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Wednesday 10 June 2020. Successful applicants will be notified by 15 June 2020.

This online course will introduce participants to the practice of game design. It will explore the different ways that digital and analogue games are designed, particularly how you can design with intent to communicate a mood, theme or message. Participants will learn game design skills - such as boxing-in, design documents and prototyping – alongside opportunities to test them out by creating their own short games. Examples will focus on game design in research-related contexts, including using games as part of your research process and to communicate research outcomes to diverse audiences.

The sessions focus on game design, how to shape mechanics and play experiences, so no technical skills are needed. Participants will create their short games using both non-digital tools and simple, free software that will be taught in the sessions.

Topics covered:

  • Game design basics
  • A chance to play and consider thoughtful games
  • Boxing in
  • Planning games
  • Making games
  • Bitsy and Twine
  • Playtesting and iteration

Format

The course will be delivered online, with live teaching sessions taking place on Zoom.

  • Weds 17 June, 4pm BST: Introduction (45 minutes)
  • Weds 24 June, 4pm BST: Game play feedback (45 minutes)
  • Weds 1 July, 4pm BST: Game design seminar (45 minutes)
  • Weds 15 July, 4pm BST: Final session (60 minutes with break)

A CRASSH blog post was created for the originally scheduled session which may be of interest to read and can be found here: http://www.crassh.cam.ac.uk/blog/post/Play-as-Research-Practice

Thu 2
(Critical) Machine Vision for the Humanities [remote delivery] new (8 of 10) Finished 15:00 - 16:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Thu 9
(Critical) Machine Vision for the Humanities [remote delivery] new (9 of 10) Finished 15:00 - 16:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Tue 14
(Critical) Machine Vision for the Humanities [remote delivery] new (10 of 10) Finished 15:00 - 17:00 Cambridge Digital Humanities Online

Leonardo Impett, Cambridge Digital Humanities

Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Friday 22 May 2020. Successful applicants will be notified by 26 May 2020.

This course will introduce graduate students, early-career researchers, and professionals in the humanities to the technologies of image recognition and machine vision, including recent developments in machine vision research in the past half-decade. The course will seek to combine a technical understanding of how machine vision systems work, with a detailed understanding of the possibilities they open to research and study in the humanities, and with a critical exploration of the social, political and ideological dimensions of machine vision.

Learning outcomes

By the end of the course, students should be able to:

  • Understand the basic tasks of machine vision, such as Image Classification, Object Detection, Image-to-Image Translation, Image Captioning, Image Segmentation etc.
  • Understand the fundamental technical operations of image processing and machine vision: the pixel encoding of images, Gaussian and convolutional filters,
  • Explore critical aspects of machine vision in a technically-informed way: e.g. the problems in algorithmic bias brought about by featureless convolutional networks
  • Develop and run their own simple machine vision and image processing pipelines, in a visual programming language compiling to Python
  • Understand the potential synergies and limitations of machine vision applications in humanities research and cultural heritage institutions
Wed 15
Game Design: an introduction for researchers [remote delivery] new (4 of 4) Finished 16:00 - 17:00 Cambridge Digital Humanities Online

Emma Reay is a third-year PhD researcher at the University of Cambridge and an associate lecturer at Anglia Ruskin University. Her current project explores depictions of children in videogames, and her research interests include representation studies, children's digital media, gaming and education, and playful activism.

Adam Dixon is a game designer and writer who makes both physical and digital games. He has worked on everything from big public games that involve running around cities to narrative video games about learning scientific skills. Much of his work has involved working with museums and research organisations such as the Wellcome Trust, Science Museum, Nottingham Trent University and the V&A. This has included designing games, using play for public research engagement and most recently, teaching teenagers to create digital games for Wellcome Collection’s Play Well exhibition. Outside of that he works and releases his own games including roleplaying games, LARPs and interactive fiction.

Applications https://www.cdh.cam.ac.uk/file/cdhgamedesign201920applicationdocx-0 should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Wednesday 10 June 2020. Successful applicants will be notified by 15 June 2020.

This online course will introduce participants to the practice of game design. It will explore the different ways that digital and analogue games are designed, particularly how you can design with intent to communicate a mood, theme or message. Participants will learn game design skills - such as boxing-in, design documents and prototyping – alongside opportunities to test them out by creating their own short games. Examples will focus on game design in research-related contexts, including using games as part of your research process and to communicate research outcomes to diverse audiences.

The sessions focus on game design, how to shape mechanics and play experiences, so no technical skills are needed. Participants will create their short games using both non-digital tools and simple, free software that will be taught in the sessions.

Topics covered:

  • Game design basics
  • A chance to play and consider thoughtful games
  • Boxing in
  • Planning games
  • Making games
  • Bitsy and Twine
  • Playtesting and iteration

Format

The course will be delivered online, with live teaching sessions taking place on Zoom.

  • Weds 17 June, 4pm BST: Introduction (45 minutes)
  • Weds 24 June, 4pm BST: Game play feedback (45 minutes)
  • Weds 1 July, 4pm BST: Game design seminar (45 minutes)
  • Weds 15 July, 4pm BST: Final session (60 minutes with break)

A CRASSH blog post was created for the originally scheduled session which may be of interest to read and can be found here: http://www.crassh.cam.ac.uk/blog/post/Play-as-Research-Practice

Wed 29
The Transkribus Guided Project new (1 of 2) Finished 16:00 - 16:30 Cambridge Digital Humanities Online

We introduce the Transkribus software system that can be taught to read handwriting from images of documents and rapidly convert it into useful digital formats. This guided course provides basic training by practical immersion in this software, which requires only basic IT skills. Transkribus was developed by READ under the Horizon 2020 funding framework and is now a co-operative. It had 20,000+ users in 2019, and is becoming a standard research tool for mass transcription of archival sources. Participants will transcribe anonymised data from pre-loaded scans of forms filled out for the French national census of 1999 in Transkribus's downloadable software interface. These manual transcriptions will help train a handwritten text recognition (HTR) model to automatically transcribe many more of these forms later. In fact, the model will eventually allow the creation of one of the largest data sets ever attempted from manuscript sources. This course is a collaboration with Transkribus and Cambridge Digital Humanities. It is funded by a Cambridge Humanities Research Grant.

August 2020

Wed 5
The Transkribus Guided Project new (2 of 2) Finished 16:00 - 17:00 Cambridge Digital Humanities Online

We introduce the Transkribus software system that can be taught to read handwriting from images of documents and rapidly convert it into useful digital formats. This guided course provides basic training by practical immersion in this software, which requires only basic IT skills. Transkribus was developed by READ under the Horizon 2020 funding framework and is now a co-operative. It had 20,000+ users in 2019, and is becoming a standard research tool for mass transcription of archival sources. Participants will transcribe anonymised data from pre-loaded scans of forms filled out for the French national census of 1999 in Transkribus's downloadable software interface. These manual transcriptions will help train a handwritten text recognition (HTR) model to automatically transcribe many more of these forms later. In fact, the model will eventually allow the creation of one of the largest data sets ever attempted from manuscript sources. This course is a collaboration with Transkribus and Cambridge Digital Humanities. It is funded by a Cambridge Humanities Research Grant.