Release 1.7.1

If we want to know how much a model has learned in training, we have to  test it. We do this with precisely defined test sets. Test sets – like the training set – contain exclusively Ground Truth. However, we make sure that this GT has never been used to train the model. So the model does not “know” this material. This is the most important characteristic of test sets. A text page that has already been used as training material will always be better read by the model than one it is not yet “familiar” with. This can easily be proved experimentally. So if you want to get valid statements about CERs and WER, you need “non-corrupted” test sets.

It is also important that a test set is representative. As long as you train an HTR model for a single writer or an individual handwriting, it’s not difficult – after all, it’s always the same hand. As soon as there are several writers involved, you have to make sure that all the individual handwritings used in the training material are also included in the test set. The more different handwritings are trained in a model, the larger the test sets will be.

The size of the test set is another factor that influences representativity. As a rule, a test set should contain 5-10% of the training material. However, this rule of thumb should always be adapted to the specific requirements of the material and the training objectives.

To illustrate this with two examples: Our model for the Spruchakten from 1580 to 1627 was trained with a training set of almost 200,000 words. The test set contains 44,000 words. This is of course a very high proportion of about 20%. It is due to the fact that material of about 300 different writers was trained in this model, which must also be represented in the test set. – In our model for the judges’ opinions of the Wismar Tribunal, there are about 46,000 words in the training set, the test set contains only 2,500 words, i.e. a share of about 5%. However, we only have to deal with 5 different writers. In order to have a representative test set, the material is sufficient.

Release 1.7.1

The Word Error Rate (WER) and Character Error Rate (CER) indicate the amount of text in a handwriting that the applied HTR model did not read correctly. A CER of 10% means that every tenth character (and these are not only letters, but also punctuations, spaces, etc.) was not correctly identified. The accuracy rate would therefore be 90 %. A good HTR model should recognize 95% of a handwriting correctly, the CER is not more than 5%. This is roughly the value that is achieved today with “dirty” OCR for fracture fonts. Incidentally, an accuracy rate of 95% also corresponds to the expectations formulated in the DFG’s Practical Rules on Digitization.

Even with a good CER, the word error rate can be high. The WER shows how good the exact reproduction of the words in the text is. As a rule, the WER is three to four times higher than the CER and is proportional to it. The value of the WER is not particularly meaningful for the quality of the model, because unlike characters, words are of different lengths and do not allow a clear comparison (a word is already incorrectly recognized if just one letter in it is not correct). That is why the WER is rarely used to characterize the value of a model.

The WER, however, gives clues to an important aspect. Because when I perform a text recognition with the aim of later performing a full text search in my document, the WER shows me the exact success rate that I can expect in my search. The search is for words or parts of words. So no matter how good my CER is: with a WER of 10%, potentially every tenth search term cannot be found.

Tips & Tools
The easiest way to display the CER and WER is to use the Compare function under Tools. Here you can compare one or more pages of a Ground Truth version with an HTR text to estimate the quality of the model.

Release 1.7.1

Ground Truth (GT) is the basis for the creation of HTR models. It is simply a typewritten copy of the historical manuscript, a classic literal or diplomatic transcription that is 100% correct – plainly “Groundt Truth”.

Any mistake in this training material will cause “the machine” to learn – among many correct things – something wrong. That’s why quality management is so important when creating GT. But don’t panic, not every mistake in the GT has devastating consequences. It simply must not be repeated too often; otherwise it becomes “chronic” for the model.

In order to ensure the quality of the GT within our project, we have set up a few fixed transcription guidelines, as you know them from edition projects. It is worthwhile to strive for a literal, character-accurate transcription. Regulations of any kind must be avoided; e.g. normalizations, such as the vocal or consonant usage of “u” and “v” or the encoding of complex abbreviations.

If the material contains only one or two different handwritings, about 100 pages of transcribed text are sufficient for a first training session. This creates a basic model that can be used for further work. In our experience, the number of languages used in the text is irrelevant, since the HTR models usually work without dictionaries.

In addition to conventional transcription, Ground Truth can also be created semi-automatically. Transkribus offers a special tool – Text2Image – which is presented in another post.