At this point the model is good enough. It includes a Windows No features added Add a feature. For my part, I have had good results with as few as lines and have not had the chance to train with more than yet. Alternatives 37 Comments 0 Reviews 0.

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If I get enough of it, I will try to statistically estimate better configurations and make a post about it. Since version 0. This will give you ocrkpus most frequent confusions, in a form similar to:.

Homemade manuscript OCR (1): OCRopy

Tomato 2, 13 By using this site, you agree to the Terms of Use and Privacy Policy. For the scholar of medieval manuscripts, it has interesting applications, and it can help in the constitution of textual databases or in the collation of witnesses. A complete refactoring of the source code in Python modules was done and released in version 0.

By the end of this post, the performance will be extremely good. Maybe this model just had bad luck. Free Web No features added Add a feature. ALN is a variant of the model output which is aligned to the truth data.

TRU is the truth data.

Which OCR Engine is better: Tesseract or OCRopus? - Stack Overflow

You train on top of an existing model using the --load ocropuss. You can also check the activity of projects in "changes" link https: In the last postwe walked through the steps in the Ocropus OCR pipeline. Other useful options are:.

If this question can be reworded to fit the rules in the help centerplease edit the question. Bodmer 68, using uniform and gaussian modes, I had the following error rates in column and line segmentation on fol. Once installed, ocgopus can be invoked by specifying the input images.

In particular, it is already being used to acquire the texts of incunabula.

If you want to correct the results from the prediction, before extracting it, you can do instead:. You can also extract it with:.

Simply take a picture of the text You can convert it to TEI or some other format, and start editing. But it was the solution!

Commercial Mac iPhone Scan documents Add a feature.

Actually, every time, in my experience, retraining an existing model, be it for print or manuscript, though it might be faster, was less effective in the end. If more precise control is needed, options can be specified on the command line to perform specific operations e.

Here, splitting the pages. The first alpha version 0. The --load options allows you to retrain on an existing model or, if you want, to split training in a few different sessionsand the -N to decide after how many iterations the training must end. For my first model, I used of the labeled lines as training data and ovropus out the other as test data.

Archived from the original on 24 December As it loops through the training data over and over again, the model gets better and better. I don't understand why this question is closed. How is that possible?

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