A Simple and stateful backend for text extraction, image extraction, noteshrinking, and making OCR of PDF files
PDFSense combines several open source PDF, image and text tools to one REST API. It tries to use sensible defaults, so that you could get good results without tinkering with the settings.
PDFSense stores the output of every endpoint as a directory tree (thats' why it's stateful). This allows you to upload original PDF once and then experiment with processing endpoints without the need to upload original data again and again.
.
├── extracted
│ └── images
│ ├── noteshrink
│ │ └── images
│ │ ├── files.txt
│ │ ├── noteshrink.cli
│ │ ├── noteshrink.log
│ │ ├── page-000.png
│ │ └── ocr
│ │ └── textpdf
│ │ ├── files.txt
│ │ ├── ocr.cli
│ │ ├── ocr.log
│ │ └── ocr.pdf
│ └── page-000.jpg
├── rendered
│ └── 100
│ ├── page-1.png
│ └── pdf
│ ├── combined
│ │ ├── full.pdf
│ │ ├── qpdf.cli
│ │ └── qpdf.log
│ ├── files.txt
│ └── images.pdf
└── typewritten_bw_aamunkoitto.pdf
PDFSense mounts 'data' directory from host machine to the container. This allows you to use following setup, where you can examine the result of each action immediately in your file browser:
Get source, build image and start
git clone https://github.com/artturimatias/PDFSense
cd PDFSense
make build
make start
Upload your first PDF:
curl -F "[email protected]" http://localhost:8200/api/uploads
Render your PDF as images with resolution 150 dpi:
curl -X POST http://localhost:8200/api/uploads/myfile.pdf/rendered/150
Now you can find rendered images from data/uploads/myfile.pdf/rendered/150
You can continue processing with rendered images. Let's rotate images and create a new PDF from rotated images:
curl -X POST http://localhost:8200/api/uploads/myfile.pdf/rendered/150/rotate/90
curl -X POST http://localhost:8200/api/uploads/myfile.pdf/rendered/150/rotate/90/pdf
Now you should have a PDF file with pages that are sideways on data/uploads/myfile.pdf/rendered/300/rotate/90/pdf/images.pdf
PDFSense uses weird but handy command path REST API. You first upload your PDF to /api/uploads, then you can continue processing by using the file id which can be found from the response. You can then continue prosessing by stacking commands in the path.
PDFSense includes also a python script, which allows you easily batch process several files.
- /rotate/:angle (integer) Rotate images (using sharp)
- /blur/:sigma (integer) Blur images (using sharp)
- /sharpen/:sigma (integer) Sharpen images (using sharp)
- /threshold/:threshold (integer) Threshold (using sharp)
- /trim/:threshold (integer) Trim images
Image processing commands without parameters:
- /grayscale Turn images to grayscale images (using sharp)
- /flip Flip images vertically (using sharp)
- /flop Flop images horizontally (using sharp)
- /negate Invert colors (using sharp)
- /extracted/images (pdfimages)
- /extracted/text
- /rendered/:resolution
- /ocr/text?lang=[LANG] Create text file per page
- /ocr/pdf?lang=[LANG] Create searchable PDF from images
- /ocr/textpdf?lang=[LANG] Create text only (invisible text) PDF
- /pdf Create pdf from images
- /combined Create searchable pdf from images by adding overlay from /ocr/textpdf end point when run after any image endpoint.
- /combined Create searchable pdf from original pdf when run after /ocr/textpdf endpoint
First we must upload the original PDF. This creates an unique file id which is used as a base path for actual processing commands.
using httpie:
http --form POST :8200/api/uploads [email protected]
using curl:
curl -F "[email protected]" http://localhost:8200/api/uploads
This returns upload id and some other information
{
"fileid": "myfile.pdf",
"filepath": "tmp/myfile.pdf/myfile.pdf",
"path":":8200/api/uploads/myfile.pdf",
"name": "myfile.pdf",
"size": 9473628,
"type": "application/pdf"
}
Now we can extract images from PDF by adding command extracted/images to the path
http POST :8200/api/uploads/myfile.pdf/extracted/images
This returns a list of images
{"files":["page-000.jpg","page-001.jpg" ... ]}
We can now refer to extracted images by path "/api/uploads/myfile.pdf/extracted/images". We can further process the result of image extraction by adding a new command path to the path.
Note that extracted images can have "strange" formats like ppm, formats which image processing endpoints can not handle. However, you can force extracted files to png format by adding "?format=png" to the end of the url.
Let's apply some processing to these images. In this case let's test OCR. We add command ocr/text to the path.
http POST :8200/api/uploads/myfile.pdf/extracted/images/ocr/text?lang=fin
This runs tesseract and creates one text file per image and additonal file called "fulltext.txt".
If the result of OCR was not satisfying, we must identify the problem and further process images. Let's say that our images had bad orientation and they must be rotated. We can do this by "sharp" command.
We just remove the previous command from path (ocr/text) and apply sharp command rotate/:ANGLE.
http POST :8200/api/uploads/myfile.pdf/extracted/images/rotate/90
After then we can try run OCR again for rotated images.
http POST :8200/api/uploads/myfile.pdf/extracted/images/rotate/90/ocr/text?lang=fin
After you have experimented different settings and you are getting decent result for couple of files, you may want to process more files with same settings. There is a simple python script (batch.py) included with PDFSense and it is located in 'python' directory.
Here is a commands that OCR files and then creates a searchable pdf by using the original PDF as a base and adding text-only PDF as overlay on it.
commands = [
'/rendered/300',
'/rendered/300/ocr/textpdf?lang=fin',
'/rendered/300/ocr/textpdf/combined'
]
So just write paths as they would be when processing files directly through API.
Here is an example that makes OCR for all files in 'pdf' directory. The result is stored in myfiles/output (--download option).
python3 python/batch.py --dir ./my_files --download
PDFSense is an API and you can use whatever tools in order to call API and process multiple files one by one.
Upload PDF and get upload id.
curl -F "[email protected]" http://localhost:8200/api/uploads
or with httpie:
http --form :8200/api/uploads [email protected]
Extracts text (pdf2text) or images (pdfimages) from PDF
http POST :8200/api/uploads/my.pdf/extracted/images?format=png
Default output is jpg, but that can be changed by setting option "format". Supported formats are jpg, png and tiff
Extracts text (pdf2text) or images (pdfimages) from PDF
http POST :8200/api/uploads/my.pdf/extracted/text
Renders images from PDF with resolution defined in path. For example:
http POST api/uploads/my.pdf/rendered/300
Default output is png, but with option '?format=jpg' endpoint outputs images in jpg format.
Sometimes orientation of crappy digitalisations could be sideways. Orientation endpoint makes it possible to divide processing paths based on orientation when making batch editing.
Call orientation endpoint after upload. The endpoint creates a directory 'orientation/[ANGLE]'. This allows processing different orientations different ways.
http POST api/uploads/my.pdf/orientation?resolution=300
This will create a command path 'api/uploads/my.pdf/orientation/0/rendered/300' if the orientation is 0. Likewise, if orientation is 90, the path would be 'api/uploads/my.pdf/orientation/90/rendered/300'.
This means that you can batch process pdf files differently based on their orientation. Just use batch.py and run it with different command sets per orientation. In other words, if orientation is 90, you have to rotate images by 90 degrees and then do the ocr etc. If orientation is 0, you do not need rotate step.
As you see, the end of the orientation path is same as if you rendered images from PDF with resolution 300 (rendered/300). The explanation is that rendered images are used for orientation detection and after detection, images are copied to rendered/300 directory, so they can be further processed without rendering again.
Use sharp for processing images. Add to extracted or rendered images path.
example: rotate rendered images 90 degree clockwise:
http POST api/uploads/my.pdf/rendered/300/rotate/90
Commands and their parameters and default values:
rotate: {'angle': 90},
blur: {'sigma':1},
sharpen: {'sigma':1},
trim: {'trim_threshold':10},
threshold:{'threshold': 128}
flip: {},
flop: {},
grayscale: {},
negate: {},
Apply noteshrink to images (excellent for improving bad b/w scans)
http POST api/uploads/my.pdf/rendered/300/noteshrink/images
Runs tesseract and output text file (text), regular PDF (pdf) or PDF with text only (textpdf).You can run all or just one. Note the language query parameter! Default language is 'eng'. Make sure you have installed tesseract language package for your language (see Dockerfile, eng, fin and swe are installed by default)
http POST api/uploads/my.pdf/rendered/300/noteshrink/images/ocr/pdf?lang=fin
Generate PDF from images. For example:
POST api/uploads/my.pdf/rendered/300/pdf
Create searchable PDF by adding text-only PDF to the PDF generated from images. This can be used for creating searchable PDF with low resolution images. Note that this creates a new PDF file. If you want to add text layer to the original file, then use /ocr/textpdf/combined -endpoint.
Run this from path where your image PDF is. For example:
POST api/uploads/my.pdf/rendered/100/pdf/combined
PDFSense scans directory tree in order to find text only PDF (produced by /ocr/textpdf -endpoint). That's why you should have only one text only PDF in your tree.
Create searchable pdf by adding text-only PDF as overlay to the copy of the original file. This can be used only after /ocr/textpdf -endpoint. Unlike other endpoints, this will create a file named by file_id (original file name).
POST ../ocr/textpdf/combined?prefix=ocr_
Optional prefix allows you to add prefix to file name.
Create a zip archive with all files and directories produced by PDFSense
Fetch all files and directories as zip archive
https://github.com/tesseract-ocr
https://github.com/lovell/sharp
https://github.com/mzucker/noteshrink
https://pypi.org/project/poppler-utils/
https://github.com/mzsanford/cld
If you just want to OCR pdf files and have searchable pdf as output, then try OCRmyPDF
First, make sure that you have set the language right (like '?lang=fin') and that you have required language pack installed (see Dockerfile). Also one possible explanation for a very bad OCR result is the wrong orientation of images. If images are "sideways", then OCR might not be able to detect text lines. For black/white scan I recommend using 'noteshrink' (/noteshrink/images) before doing OCR.
The command path "extracted/images" takes images from PDF as their native resolution and format by using Poppler util called "pdfimages". In some cases this might produce images being badly orientated or images without cropping that was present in original PDF. However, images from "rendered/[RESOLUTION]" are rendered from PDF by pdftoppm. These images are like screenshots of pages with desired resolution. The cropbox option is sometimes useful in order to get images in their cropped (visible in PDF) form.
docker: Error response from daemon: invalid mount config for type "bind": bind source path does not exist: /data.
This happens when you run "sudo make start". The variable PWD is not then set. You either run sudo with E option:
sudo -E make start
Or, you can make sure that you can run docker commands as a regular user: https://docs.docker.com/engine/install/linux-postinstall/
Yes, just create following file structure in data directory of PDFSense:
uploads/my_image_pdf/extracted/images
Then copy your files to the images directory and then run:
http POST :8200/api/uploads/my_image_pdf/extracted/images/pdf
Note that images must jpg files, or png files WITHOUT alpha channel.