* Feature: store barcode contents, list and search them
New setting PAPERLESS_CONSUMER_STORE_BARCODE_VALUES (off by default)
stores all barcodes found during consumption with the document: page,
type and content. They are listed on the metadata tab with a copy
button, returned by the documents API and searchable with barcodes:
in the advanced search. Versions keep their own barcodes, reprocessing
reads them again. Refs #9898
* Tests: cover the remaining barcode branches
Covers unchanged and failing barcode reads on reprocessing, unsupported files and DocumentBarcode.__str__, and uses toHaveLength in the barcode list spec as suggested by SonarCloud.
* Address review: keep barcodes when they can't be read, format choices
- Reprocessing and new versions keep the stored barcodes when the file
can't be scanned or the scan fails, and replace them atomically.
- Format is a TextChoices of the zxing-cpp formats, with a test.
- Shared scan code, latest_version helper, TypedDict, serializer reuse.
- Barcodes in the split manifest, export/import tests, pytest-style tests.
* Barcode tests: TIFF reprocess, format check both ways, fixtures
- Reprocessing a TIFF with TIFF support off keeps the stored barcodes.
- The format test also fails when zxing-cpp drops a format.
- Fixtures in place of the sample dir mixin, plugin-level disabled test.
- Format labels aren't translated, OpenAPI enum named BarcodeFormatEnum.
* Review: module-level zxing reader, shorter barcode docs
- read_barcodes_zxing is a module-level function used by scan_pdf.
- Drop the trivial __str__ test, mark it no cover.
- Shorten the barcode docs and remove the duplicate in configuration.md.
* Fix header
* Use utility class
* Return barcodes from the metadata endpoint only
Drop the barcodes field and its prefetches from the document serializer,
as agreed in the review. Also remove the now empty component stylesheet.
---------
Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>
* Preprocesses classifier content with Tantivy instead of NLTK
Tokenizing and stemming now happen in one Rust call instead of NLTK's
Python tokenizer and per word stemming, which also removes the Redis
backed stem cache from every preprocessing call. The output matches the
NLTK pipeline closely; tokens containing digits are now stemmed, and the
English stop words follow Snowball's list.
Stemming and stop word removal apply whenever the OCR language is one of
the supported classifier languages, so PAPERLESS_ENABLE_NLTK and
PAPERLESS_NLTK_DIR are removed.
* Copies packages instead of hardlinking them in backend CI, some NLTK thing
* Adds a normalization to NFC to better fit what Tantivy expects
* Security: prevent XSS with storage path template rendering
* Security: prevent XSS svg uploads
* Security: force attachment disposition for logo
* Add suggestions from code review
* Improve SVG validation with allowlist for tags and attributes
* Feature: collate two single-sided scans
Some ADF only support single-sided scans, making scanning
double-sided documents a bit annoying.
This new feature enables Paperless to do most of the work,
by merging two seperate scans into a single one, collating
the even and odd numbered pages.
* Documentation: clarify that collation is disabled by default
* Apply suggestions from code review
Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>
* Address code review remarks
* Grammar fixes
---------
Co-authored-by: shamoon <4887959+shamoon@users.noreply.github.com>
* also split documents when an ASN barcode is found
* linter
* fix test case parameters
* avoid pre-python-3.9 features
* simplify dict-creation in tests
* simplify dict-creation in tests for empty dicts
* Add test cases for the splitting by ASN barcode feature
* deleted supporting files for test case construction
I've broken out the OCR-specific code from the consumers and dumped it
all into its own app, `paperless_tesseract`. This new app should serve
as a sample of how to create one's own consumer for different file
types.
Documentation for how to do this isn't ready yet, but for the impatient:
* Create a new app
* containing a `parsers.py` for your parser modelled after
`paperless_tesseract.parsers.RasterisedDocumentParser`
* containing a `signals.py` with a handler moddelled after
`paperless_tesseract.signals.ConsumerDeclaration`
* connect the signal handler to
`documents.signals.document_consumer_declaration` in
`your_app.apps`
* Install the app into Paperless by declaring
`PAPERLESS_INSTALLED_APPS=your_app`. Additional apps should be
separated with commas.
* Restart the consumer
jurassicparkicecreamandshamoon
Trenton H
Christoph Schober
Antoine Mérino
Dennis Brakhaneandshamoon
Bastian Machek
Fabian Ohler
Peter Kappelt
Trenton H
Florian Brandes
jonaswinkler
Daniel Quinn
Florian Harr