1. Install `screen`. We will use this to execute commands in their own terminal session.
## Build your own image
1. Clone this repository and navigate into it.
2. Build the image from the dockerfile. `docker build -t <image_name>:<tag> .` For example: `docker build -t ocr_container:latest .`
Alternatively build directly from git.
1. Use the following command to build directly from gitLab. `docker build -t <image_name>:<tag> https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr.git`.
## Folder setup
1. Create input and output folders for the OCR files.
1. Run container from an image. <contianer_name> and /some/path are the same as mentioned in the step folder setup. We are creating two volumes based on the folder paths provided in the section Folder setup.
1. Place some files inside the folder _files\_for\_ocr_. Files can either be multipage tiffs or PDF files. One folder per file is needed. Files should all be of the same language.
2. Start a screen session with `screen -dmS <container_name>`
3. Enter the screen session with `screen -r <container-name>`. (Try this if there is an error. `script -q -c "screen -r <container-name>" /dev/null`).
4. Start the OCR process for all files placed in _files\_for\_ocr_ with `docker exec -it <container-name> ocr -i files_for_ocr -o files_from_ocr -l <sprachcode>`.
Valid language codes are:
- deu (German)
- deu_frak (German Fraktur)
- eng (English)
- enm (Middle englisch)
- fra (French)
- frm (Middle french)
- por (Portuguese)
- spa (Spanish)
## Exit an re-enter the current running OCR process
1. You can leave the currently running OCR process by pressing `ctrl + a + d` and thus leaving the screen session.
2. Re-enter the screen session to check the status of the running OCR job with `screen -r <container-name>`. (Try this if there is an error. `script -q -c "screen -r <container-name>" /dev/null`).
Just append the needed data file URL after line 56 in the Dockerfile following the same syntax.
The standard traineddata for various languages can be found under https://github.com/tesseract-ocr/tessdata. Click on one of the languages and copy the link from the download button. The URL for Afrikaans (afr) would be for example https://github.com/tesseract-ocr/tessdata/raw/4.00/afr.traineddata.
The more accurate but slower traineddata can be found under https://github.com/tesseract-ocr/tessdata_best. Click on one of the languages and copy the link from the download button. The URL for Afrikaans (afr) would be for example https://github.com/tesseract-ocr/tessdata_best/raw/master/afr.traineddata.
Traineddata for fraktur fonts can also be found in the standard tessdata repository https://github.com/tesseract-ocr/tessdata.
The Dockerfile section for the traineddata with added language support for Afrikaans would look like this:
```
RUN echo "deb https://notesalexp.org/tesseract-ocr/stretch/ stretch main" >> /etc/apt/sources.list && \