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Patrick Jentsch
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README.md
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README.md
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# Installation
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# OCR
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## Install additional packages
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1. Install `screen`. We will use this to execute commands in their own terminal session.
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## Build image
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## Build your own image
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1. Clone this repository and navigate into it:
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```
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git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr.git && cd ocr
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```
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2. Build image:
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```
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docker build -t sfb1288inf/ocr:latest .
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```
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1. Clone this repository and navigate into it.
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2. Build the image from the dockerfile. `docker build -t <image_name>:<tag> .` For example: `docker build -t ocr_container:latest .`
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Alternatively build from git without cloning:
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1. Build image:
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```
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docker build -t sfb1288inf/ocr:latest https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr.git
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```
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Alternatively build directly from git.
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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`.
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## Download prebuilt image
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## Folder setup
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The GitLab registry provides a prebuilt image. It is automatically created, utilizing the conquaire build servers.
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1. Create input and output folders for the OCR files.
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2. `mkdir -p /some/path/<container-name>/ocr/files_for_ocr /some/path/<image_name>/ocr/files_from_ocr`
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1. Download image:
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```
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docker pull gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:latest
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```
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## Run the container
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## Run
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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.
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1. Create input and output directories for the OCR software:
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```
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mkdir -p /<mydatalocation>/files_for_ocr /<mydatalocation>/files_from_ocr
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```
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2. Place your files inside the `/<mydatalocation>/files_for_ocr` directory. Files can either be
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multipage TIFF (.tiff, .tif) or PDF (.pdf) files. Files should all contain text
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of the same language.
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3. Start the OCR process.
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```
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docker run \
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--name <container-name> \
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-dit \
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-v /some/path/<container-name>/files_for_ocr:/root/files_for_ocr \
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-v /some/path/<container-name>/files_from_ocr:/root/files_from_ocr \
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<image_name>
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--rm \
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-it \
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-v /<mydatalocation>/files_for_ocr:/files_for_ocr \
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-v /<mydatalocation>/files_from_ocr:/files_from_ocr \
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sfb1288inf/ocr:latest \
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-i /files_for_ocr \
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-o /files_from_ocr \
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-l <languagecode>
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```
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The specified below `sfb1288inf/ocr:latest` are described in the OCR arguments part.
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4. Check your results in the `/<mydatalocation>/files_from_ocr` directory.
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# Usage
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### OCR arguments
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## Start an OCR job
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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.
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2. Start a screen session with `screen -dmS <container_name>`
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3. Enter the screen session with `screen -r <container-name>`. (Try this if the error "Cannot open your terminal '/dev/pts/0' - please check." appears: `script -q -c "screen -r <container-name>" /dev/null`).
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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>`.
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`-i path`
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* Sets the input directory using the specified path.
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* required = True
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Valid language codes are:
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- deu (German)
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- deu_frak (German Fraktur)
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- eng (English)
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- enm (Middle englisch)
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- fra (French)
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- frm (Middle french)
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- por (Portuguese)
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- spa (Spanish)
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`-o path`
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* Sets the output directory using the specified path.
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* required = True
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### Additional OCR arguments
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Below we will describe all available pipeline arguments that can be used.
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`-l languagecode`
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* Tells tesseract which language will be used.
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* options = deu (German), deu_frak (German Fraktur), eng (English),
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enm (Middle englisch), fra (French), frm (Middle french), por (Portuguese),
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spa (Spanish)
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* required = True
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`--keep-intermediates`
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* If set, all intermediate files created during the OCR process will be
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kept.
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* default = False
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* required = False
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- **_-i some/path_**
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- Sets the input directory using the specified path.
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- required = True
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`--nCores corenumber`
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* Sets the number of CPU cores being used during the OCR process.
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* default = min(4, multiprocessing.cpu_count())
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* required = False
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- **_-o some/path_**
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- Sets the output directory using the specified path.
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- required = True
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- **_-l valid_language_code_**
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- Tells tesseract which language will be used.
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- required = True
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- **_--keep-intermediates**
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- Optional argument. If set all intermediate filese created during the OCR process will be kept.
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- default = False
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- required = False
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- **_--nCores_**
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- Sets the number of CPU cores being used during the OCR process.
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- default = min(4, multiprocessing.cpu_count())
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- required = False
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- **_--skip-binarization_**
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- Used to skip binarization with ocropus.
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- If skiped, only the tesseract binarization is used.
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`--skip-binarization`
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* Used to skip binarization with ocropus. If skipped, only the tesseract
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binarization is used.
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* default = False
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Example with all arguments used:
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```
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docker run \
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--rm \
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-it \
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-v "$HOME"/ocr/files_for_ocr:/files_for_ocr \
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-v "$HOME"/ocr/files_from_ocr:/files_from_ocr \
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sfb1288inf/ocr:latest \
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-i /files_for_ocr \
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-o /files_from_ocr \
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-l eng \
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--keep_intermediates \
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--nCores 8 \
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--skip-binarization
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```
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`docker exec -it <container-name> ocr -i files_for_ocr -o files_from_ocr -l deu --keep_intermediates --nCores 8`
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# Additional language models for OCR
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Additional language models can be easily installed. Just add them analogical to the existing models to the `Dockerfile`.
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## Exit and re-enter the current running OCR process
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1. You can leave the currently running OCR process by pressing `ctrl + a + d` and thus leaving the screen session.
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2. Re-enter the screen session to check the status of the running OCR job with `screen -r <container-name>`. (Try this if the error "Cannot open your terminal '/dev/pts/0' - please check." appears: `script -q -c "screen -r <container-name>" /dev/null`).
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The standard language models 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.
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# Use prebuilt image
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Download via regestry function with login or deploy token.
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The more accurate but slower language models 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.
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# Add additional traineddata for OCR of additional languages.
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Additional traineddata can be easily added to the Dockerfile.
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Just append the needed data file URL after line 56 in the Dockerfile following the same syntax.
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Language models for fraktur fonts can also be found in the standard tessdata repository https://github.com/tesseract-ocr/tessdata.
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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.
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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.
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Traineddata for fraktur fonts can also be found in the standard tessdata repository https://github.com/tesseract-ocr/tessdata.
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The Dockerfile section for the traineddata with added language support for Afrikaans would look like this:
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The `Dockerfile` section for the language models with added language support for Afrikaans would look like this:
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```
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RUN echo "deb https://notesalexp.org/tesseract-ocr/stretch/ stretch main" >> /etc/apt/sources.list && \
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wget -O - https://notesalexp.org/debian/alexp_key.asc | apt-key add - && \
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apt-get update && \
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apt-get install -y --no-install-recommends tesseract-ocr && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/afr.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/deu.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata/raw/master/deu_frak.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/eng.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/enm.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/fra.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/frm.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/ita.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/por.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/spa.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata && \
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/afr.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata
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```
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wget -nv https://github.com/tesseract-ocr/tessdata_best/raw/master/spa.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata
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```
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