Docspell processes each uploaded file. Processing involves extracting archives, extracting text, anlyzing the extracted text and converting the file into a pdf. Text is analyzed to find metadata that can be set automatically. Docspell compares the extracted text against a set of known meta data. The Meta Data page allows to manage this meta data:
Items can be tagged with multiple custom tags (aka labels). This allows to describe many different workflows people may have with their documents.
A tag can have a category. This is meant to group tags together. For example, you may want to have a tag category doctype that is comprised of tags like bill, contract, receipt and so on. Or for workflows, a tag category state may exist that includes tags like Todo or Waiting. Or you can tag items with user names to provide "assignment" semantics. Docspell doesn't propose any workflow, but it can help to implement some.
Docspell can try to predict a tag for new incoming documents automatically based on your existing data. This requires to train an algorithm. There are some caveats: the more data you have correctly tagged, the better are the results. So it won't work well for maybe the first 100 documents. Then the tags must somehow relate to a pattern in the document text. Tags like todo or waiting probably won't work, obviously. But the typical "document type" tag, like invoice and receipt is a good fit! That is why you need to provide a tag category so only sensible tags are being learned. The algorithm goes through all your items and learns patterns in the text that relate to the given tags. This training step can be run periodically, as specified in your collective settings such that docspell keeps learning from your already tagged data! More information about the algorithm can be found in the config, where it is possible to fine-tune this process.
Another way to have items tagged automatically is when an input PDF file contains a list of keywords in its metadata section (this only applies to PDF files). These keywords are then matched against the tags in the database. If they match, the item is tagged with them.
The organization entity represents an non-personal (organization or company) correspondent of an item. Docspell will choose one or more organizations when processing documents and associate the "best" match with your item.
The person entitiy can appear in two roles: It may be a correspondent or the person an item is about. So a person is either a correspondent or a concerning person. Docspell can not know which person is which, therefore you need to tell this by checking the box "Use for concerning person suggestion only". If this is checked, docspell will use this person only to suggest a concerning person. Otherwise the person is used only for correspondent suggestions.
Document processing uses the following properties:
The website and e-mails can be added as contact information. If these three are present, you should get good matches from docspell. All other fields of an organization and person are not used during document processing. They might be useful when using this as a real address book.
The equipment entity is almost like a tag. In fact, it could be replaced by a tag with a specific known category. The difference is that docspell will try to find a match and associate it with your item. The equipment represents non-personal things that an item is about. Examples are: bills or insurances for cars, contracts for houses or flats.
Equipments don't have contact information, so the only property that is used to find matches during document processing is its name.
Folders provide a way to divide all documents into disjoint subsets. Unlike with tags, an item can have at most one folder or none. A folder has an owner – the user who created the folder. Additionally, it can have members: users of the collective that the owner can assign to a folder.
When searching for items, the results are restricted to items that have either no folder assigned or a folder where the current user is owner or member. It can be used to control visibility when searching. However: there are no hard access checks. For example, if the item id is known, any user of the collective can see it and modify its meta data.
One use case is, that you can hide items from other users, like bills for birthday presents. In this case it is very unlikely that someone can guess the item-id.
While folders are not taken into account when processing documents, they can be specified with the upload request or a source url to have them automatically set when files arrive.
Docspell allows to create your own fields. Please see this page for more information.
An important setting is the language of your documents. This helps OCR and text analysis. You can select between various languages. The language can also specified with each upload request.
Go to the Collective Settings page and click Document Language. This will set the lanugage for all your documents. It is not (yet) possible to specify it when uploading.
The language has effects in several areas: text extraction, fulltext search and text analysis. When extracting text from images, tesseract (the external tool used for this) can yield better results if the language is known. Also, solr (the fulltext search tool) can optimize its index given the language, which results in better fulltext search experience. The features of text analysis strongly depend on the language. Docspell uses the Stanford NLP Library for its great machine learning algorithms. Some of them, like certain NLP features, are only available for some languages – namely German, English and French. The reason is that the required statistical models are not available for other languages. However, docspell can still run other algorithms for the other languages, like classification and custom rules based on the address book.
More information about file processing and text analysis can be found here.