Category Archives

18 Articles

Posted by Anna Brandt on

Region Grouping

Since the version update 1.14.0 there is a new function to configure the layout analysis. It is about the arrangement of the text regions, called ‘Region grouping’. Now you can configure if they should be grouped around baselines or if all lines should be in one TR.

With the first mentioned setting it can happen that many small TRs appear at the edge of the image or in the middle of it, even if there is actually only one text block. This problem can be solved in a further step with the Remove small Textregions.

On the other hand, if only one text region is set, really all baselines are in this text region, even those that are otherwise marginal and even vertical BL. As long as the setting ‘Heterogeneous’ is selected for ‘Text orientation’, the layout analysis also recognizes the vertical lines in the same TR with the horizontal ones. It can be seen that the LA would normally recognize multiple TR. In fact, the reading order for the lines is still divided as if they were in their own text regions. The main paragraph is usually TR 1, so the RO starts there. The other baselines are placed at the back, even if they are at the side of the main text and could therefore be placed between them.

To decide which setting is better for you, you have to try it out. For pages that have only one text block, the second setting is of course advantageous, because all the small TR do not appear. It could also be that you have to choose different settings within one document.

Posted by Dirk Alvermann on

Merge small Base Lines

This tool is – like “Remove small text lines” – distributed with version 1.12.0 of Transkribus. The idea behind it is very interesting.

Maybe you have had problems with “torn” lines in the automatic line detection (Citlab Advanced Layout Analysis). We have mentioned in an earlier post how annoying this problem can be.

So the expectations for such a great thing were of course high. But after a short time we realized that its use needs some practice and that it cannot be used everywhere without problems.

Here we show a simple example:

The Citlab Advanced Layout Analysis detected five “superfluous” text regions on the page and just as many “torn” base lines. In such a case you should first remove the redundant text regions with “remove small text regions” and then start the automatic merge tool.

Tips & Tools
Be careful with complicated layouts. You must always check the result of “merge small text lines”, because often base lines are merged that do not belong together (from lines with different reading order).

Posted by Dirk Alvermann on

Remove small text lines

Release 1.12.0

Many of you probably know the tool “Remove small text regions“, which has been available at Transkribus for the last year. Now his little brother “Remove small text lines” is coming. Finally – a tool that many users have been hoping for for a long time.

With the Citlab Advanced Layout Analysis (even on quite “normal” pages) it happens again and again that textregions or baselines are recognized where we don’t need or want them.

Often “mini-baselines” are recognized in decorated initials or between the individual lines. The HTR model of course can’t do anything with these during text recognition and the transcript will contain “empty” lines. With this tool you can easily and automatically delete these baselines

Try it yourself. We have had the best experience with this if we set the threshold to 0.05.

Posted by Anna Brandt on

Transcribing without layout analysis?

Release 1.10.1

We have emphasized in previous posts how important LA is. Without it, an HTR model, no matter how good it is, has no chance of transcribing a text properly. The steps of automatic LA (or a P2PaLA model) and HTR are usually initiated separately. Now we noticed that when an HTR model runs over a completely new or unedited page, the program automatically executes an LA.

This LA runs with the default settings of CITLab-Advanced LA. On pure pages, fewer lines have to be merged and sometimes more than one text region is recognized.

But it also means that only horizontal text is recognized. We had the same problem with our P2PaLA models. Everything that is slanted or vertical cannot be recognized this way. To do this, the LA must be initiated manually, with the setting ‘Text Orientation’ set to ‘Heterogeneous’.

Interestingly, the HTR results are better with this method than with an HTR that has been run over a corrected layout analysis. We have calculated the CER for some pages to show this.

Thus this method is a very good alternative, especially for pages with an uncomplicated layout. You save time, because you only have to initiate one process, and in the end you have a better result.

Posted by Anna Brandt on

Tools in the Layout tab

Release 1.10.

The layout tab has two more tools, which we did not mention in our last post. They are especially useful for correcting the layout and save you from annoying detail work.
The first one corrects the Reading Order. If one or more text regions are selected, this tool automatically arranges the child shapes, in this case the lines and baselines, according to their position in the coordinate system of the page. So the reading order starts at the top left and continues counting from there to the bottom right.  In the example below, a TR was split but the RO of the marginal notes got mixed up. This tool saves you now from renaming each BL individually.

The second tool (“assign child shapes”) helps to assign the BL to the correct TR. This can happen when cutting text regions or baselines that run through multiple TRs. Each BL then has to be marked in the layout tab and moved to the correct TR. For assigning them automatically to the corresponding TR you just select the TR where the BLs belong to and start the tool.

Posted by Anna Brandt on

P2PaLA – line detection and HTR

Release 1.9.1

As already mentioned in a previous post, we noticed in the course of our project that the CITLabAdvanced-LA does not optimally identify the layout in our material. This happens not only on the ‘bad’ pages with mixed layouts, but also on simple layouts, i.e. on pages without any marginalias at the edge, great deletions in the text or similar. Here the automatic LA recognizes the TR correctly, but the baselines are often faulty.

This is not only confusing when the full text is displayed later; an insufficient LA also influences the result of the HTR. No matter how good your HTR model is: if the LA does not offer adequate quality, it is a problem.

The HTR does not read the single characters, but works line based and should recognize patterns. But if the line detection did not identify the lines correctly (in case letters or words were not recognized by the LA) this often produces wrong HTR results. This can have dramatic effects on the accuracy rate of a page or an entire document, as our example shows.

1587, page 41

For this reason we have trained a P2PaLA model which also detects BLs. That was very helpful. It is not possible to calculate statistics like CERs for these layout models, but from the visual point it seems to work almost error-free on ‘simple’ pages. In addition, a postprocessing is no longer necessary in many cases.

The training material for such a model is created in a similar way to models that should recognize TRs only. The individual baselines do not have to be tagged manually for the structural analysis, even if the model does so later in order to assign them to the tagged TR. With the support of the Transkribus team and a training material of 2500 pages, we were able to train the structural model that we use today instead of the standard LA.

Posted by Anna Brandt on

P2PaLA – Postprocessing

Release 1.9.1

Especially at the beginning of the development of a structure model, it occurred to us that the model recognized every irregularity in the layout as a TR. This leads to excessive – and unnecessary – many text regions. Many of these TRs were also extremely small.

The more training material you invest, the smaller the problem. In our case these mini TRs disappeared, after we had trained our model with about 1000 pages. Until then, they are annoying because removing them all by hand is tedious.

To reduce this labour you have two options. Firstly, starting the P2PaLA you can determine how large the smallest TR is allowed to be. For this you have to select the corresponding value in the “P2PaLA structure analysis tool” before starting the job (“Min area”).

If this option does not bring the expected success, there is the option “remove small textregions”. You will find this on the left toolbar, under the item “other segmentation tools”. In the menu you can set the pages on which the filter should run as well as the size of the TR to be removed.  The size is calculated in “Threshold percentage of image size”. Here the value can be calibrated finer than with the above mentioned option. If the images, as with our material, often have small notes, for example the marginalias where there is only a single word in a TR, then the smallest or second smallest value possible should be chosen. We usually use the “Threshold percentage” of 0.005.

Even with a good structural model, it may still be possible that individual TRs have to be manually merged, split or removed, but to a much lesser extent than the standard LA would require.

Tips & Tools
Important: If you want to be sure that you don’t remove too many TRs, you can start with a “dry run”. Then the number of potentially removable TRs will be listed. As soon as you uncheck the box, the affected TRs will be deleted immediately.

Posted by Anna Brandt on

P2PaLA – Training for Textregions

Release 1.9.1

At another place of these blog you can find information and tips for structure tagging. This kind of tagging can be good for a lot of things – the following is about its use for an improved layout analysis. Because structure tagging is an important part of training P2PaLA models.

With our mixed layouts the standard LA simply had to fail. The material was too extensive for a manual creation of the layout. So we decided to try P2PaLA. For this we created training material for which we selected particularly “difficult” but at the same time “typical” pages. These were pages that contained, in addition to the actual main text, comments and additions and the like.

coll: UAG Strukturtagging, doc. UAG 1618-1, image 12

For the training material only the correctly drawn and tagged text regions are important. No additional line detection or HTR is required. However, it doesn’t bother either, so you can include pages that have already been completely edited in the training. However, if you take new pages on which only the TR has to be drawn and tagged, you’ll be faster. Then you can prepare eighty to one hundred pages for training in one hour.

While we had tagged seven different structure types with our first model, we later reduced the number to five. In our experience, a too strong differentiation of the structure types has a rather negative effect on the training.

Of course, the success of the training also depends on the amount of training material you invest. According to our experience (and based on our material) you can make a good start with 200 pages, with 600 pages you get a model you can already work with; from 2000 pages on it is very reliable.

Tips & Tools
When you create the material for structure training, it is initially difficult to realize that this is not about content. That means no matter what the content is, the TR in the middle is always the paragraph. Even if there is only one note in the middle and the concept is much longer and more important. This is the only way to really recognize the necessary patterns during training.

Posted by Dirk Alvermann on

P2PaLA vs. standard LA

Release 1.9.1

In the previous post we described that if the document layouts are very complicated, the standard LA in Transkribus does not always provide good results. But for a perfect HTR result you need a perfect LA.

Especially in the documents of the 16th and early 17th century the CITlab Advanced LA could not convince us. It was clear to us from the beginning that the standard LA wouldn’t identify the more complex layouts (text regions) in a differentiated way. However, it was the line detection that ultimately failed to meet our demands in these documents.

An example of how (in the worst case) the line detection of the standard LA worked on our material can be seen here:

1587, page 41

This may be an isolated case. However, if you process large quantities of documents in Transkribus, such cases may occur more frequently. In order to be able to evaluate the problem correctly, we have therefore recorded representative error statistics on two bundles of our material. It has been found that the standard LA here worked with an average of 12 errors in the line detection per page (see graph below, 1598). This of course has undesirable effects on the HTR result, which we will describe in more detail in the next post.

Posted by Dirk Alvermann on

P2PaLA or structure training

Release 1.9.1

Page-to-page layout analysis (P2PaLA) is a form of layout analysis that can be trained for indivudual models- similar to the HTR. You can train structure models either to recognize textregions only or textregions with baselines. They therefore fulfill the same functions as the standard layout analysis (CITlabAdvanced). The P2PaLA is particularly suitable if a document has many pages with mixed layouts. The standard layout analysis usually recognizes just one TR – and this can lead to problems with the reading order of the text.

With the help of a structure training, the layout analysis can learn where and how many TRs it should recognize.

The CITlab Advanced LA often had problems to identify the text regions in a differentiated way on our material. That’s why we experimented with P2PaLA early on in our project. First, we tried out structural models that exclusively set text regions (Main text, marginal notes, footnotes etc.). Within the TRs thus generated, we worked with the usual line detection. However, the results were not always satisfactory.

The BLs were often too short (at the beginning or the end of the line) or were torn many times – even on pages with simple layouts. Therefore we trained another one with an included recognition of the BLs, based on our already working P2PaLA model. Our newest model recognizes all ‘simple’ pages almost without any errors. For pages with very complex layouts, the results still have to be corrected, but with much less effort than before.