Anna Brandt


Posted by Anna Brandt on

Textregions

Release 1.7.1

Usually, the automatic CITlab Advanced Layout Analysis in its standard setting will recognize a single Text Region (TR) on an image with the corresponding baselines.
However, there are also simple layouts where the use of several TRs is recommended, e.g. if there are marginal notes or footnotes and similar recurring elements. As long as these text areas, which differ in content and structure, are contained in a single TR, the layout analysis simply counts the lines from top to bottom.

This “Reading Order” does not take into account where a text actually belongs in terms of content (e.g. an insertion), but only where it is graphically located on the page. Correcting an automatically generated but unsatisfactory Reading Order is boring and often time-consuming. The problem can easily be avoided by creating several text regions in which the related texts and lines are well kept like in a box.
To do this, you create the TRs manually at the appropriate places. Afterwards start the line detection with CITlab Advanced to add the baselines automatically.

Tips & Tools
If you have drawn the TRs manually and want to have the baselines drawn automatically by CITlab Advanced LA, you should first uncheck the box “Find Textregions”. Otherwise the manually drawn TRs will be replaced immediately. You should also make sure that none of the individual text regions is activated, otherwise only these will be edited.

Posted by Anna Brandt on

Elements

Release 1.7.1

For handwritten text recognition (HTR), automatic layout analysis is essential – no text recognition without layout analysis.
The layout analysis ensures that the image is divided into different areas, those that do not need further attention and others that contain the text to be recognized. These areas are called “Text Regions” (TR, green in the image). Transkribus needs “Baselines” (BL, red in the image) to recognize characters or letters within the text regions.
They are drawn underneath each text line. Baselines are surrounded by their own region, which is called “Line” (blue in the image). It has no practical relevance for the user. The three elements Text Region – Line – Baseline have a parent-child relationship to each other and cannot exist without the respective parent element – no baseline without line and no line without text region. One should know these elements, their functions and their relationship to each other, especially if you have to work on the layout manually.

Manual layouts should rather be an exception than the rule. For most use cases, Transkribus has an extremely powerful tool – the “CITlab Advances Layout Analysis”. It is the standard Transkribus model that has been used successfully since 2017. In most cases it delivers great results in automatic segmentation. This automatic layout analysis can be used for a single page, a selection of pages or an entire document.

All elements for segmentation can also be set, modified and edited manually, which is recommended in more complex layouts. An extensive toolbar is available for this purpose.

Posted by Anna Brandt on

Material

Release 1.7.1

Successful handwriting text recognition depends on four factors:

– Quality of Originals
– Quality of digital copies
– Reliable layout analysis and segmentation of image areas containing the text to be recognized
– Performance of the HTR models, “reading” the handwriting

Our blog will provide regular field reports on all these points. First of all, here are some general remarks.
Basically you can edit all handwritten documents with the tools available in Transkribus. Neither the used character system (Latin, Greek, Hebrew, Russian, Serbian etc.) nor the language is a criterion – the “models” can “learn” almost everything.
However, the quality of the originals has a big effect on the result. In other words – heavily soiled, completely faded or blackened documents have less chances for automatic text recognition than clean, strong writings.
Completely muddled  text layouts, i.e. with horizontal and vertical or diagonal lines, numerous marginal notes or insertions and text between the lines, cause more problems for the automatic layout analysis than chancellery copies. And more problems means more work for the editors.
When selecting the material, one should therefore consider the challenges it poses for the available tools and the individual work areas. This can only be done with a little experience.

In our project, multilingual documents from the 16th to 20th centuries are processed with varying degrees of difficulty. We are glad to share our experience.

Posted by Anna Brandt on

What you find here and what you don’t

This blog mainly reports about our work with Transkribus. In addition, we also present the project workflow and our experience with the scanning processes, the applied parameters, the creation of structural and metadata and the presentation of the project results in the Viewer of the Digital Library Mecklenburg-Vorpommern.

This blog is not a manual. So don’t expect us to give step-by-step instructions for individual tasks that can be done in Transkribus (although we sometimes do). But there are a lot of good and proven How-To’s, which the Transkribus team and users have developed over the past years. Here you can read about practical experiences and some tips & tricks.

Transkribus now has two interfaces: the “Expert Client”, which you can download here, and the Web User Interface (WebUI), which you can reach at this address. This blog is almost exclusively about the Expert Client, because it provides the full functionality needed to handle challenging projects. Under which circumstances and why the use of the WebUI is nevertheless useful and appropriate, we explain here .

Our experiences are based on a medium-sized large-scale-project. Here approx. 250,000 images are processed. Our focus is accordingly aligned. We use the possibilities of Transkribus to open up large quantities of documents through handwritten text recognition (HTR), to enrich them with content and to make them available online. Searchability is to be made possible by means of full text search or keyword spotting (KWS). The type of methods used and the demands placed on the results are aligned to this goal. Projects on a smaller scale may use differentiated and more subtle methods; nevertheless, there are some useful experiences for them as well.

Tips & Tools
Recommendation for further reading: Günter Mühlberger, Tamara Terbul: Handschriftenerkennung für historische Schriften. Die Transkribus Plattform