These improvements include :
   * better address reporting
   * better handling of Social Security Number and Temporary Technical Number
     for foreign employees
   * add more checks and UI error messages to report wrong values to the user
     before he submits the DSN
   * fix bugs for some edge cases

When aggregating Paysheets using a dialog, we don't want to aggregate deleted ones.

In `sheet_per_report_section` mode if the report do not have any fields,
we use to display an empty sheet. It was confusing and not useful.

Allow to export a selection of packing lists lines moving products in
spreadsheet format

This commit contain a testing business template for the receipt recognition module
test the "Receipt" type update as well as the OCR success and fail on a set of poor

This commit contain binary files that are test images..

This commit contain the business template that take a receipt image as a source,
binarize then segmentize it, and apply OCR on it. It then extract the meaning
with regular expressions. The image should already be loaded inside the
image module before it can be read.

The business template contain:
	* The receipt recognition module
	* An extension containing the code that binarize, crop and
	  segmentize the image then analize it.
	* A new type "Receipt" that contain a source image and the
	  field that contain the "total" value
	* A portal skin folder containing the extension externalMethods
	  aswell as the conversion script that call the recognition and
	  update the Receipt "total" field

Improvements (not limited to this list):
	- Easier loading of picture: directly from the receipt page.
	- Easier loading of picture 2: from phone with OfficeJS
	  (or any renderJS) application?
	- Detect when images are sideway and rotate them straight
	- Better "boxing" and segmentation: some lines are deleted from
	  the original image during the segmentation when they are too
	  close from other
	- Modify the neural network (lstm) to increase weight of signs
	  like $, euro, / and numbers
	- Use of a faster/smaller neural network: Most of the time is
	  lost with the loading of the neural network
	- Caching the neural network: See previous statement.
	- Extract currency, date and receipt emettor.
	- Use a neural network for the meaning extraction?

Avoid iterating over all columns known to catalog to then restrict to a
single table by using SQLCatalog API.
Only check for one range column suffix as code anyway relies on the triplet
of columns to be consistently present. Document this in the code and get
rid of now-unneeded range_column_set mechanism.

str.encode() first performs an implicit conversion to unicode using
sys.getdefaultencoding(), which is usually 'ascii'. The 'isort' module
changes the default encoding to utf-8, leading to UnicodeEncodeError
instead of UnicodeDecodeError. Let's simplify all this.

These methods will be used in erp5 catalog view in restricted environment.
Loosen the security on methods which we will use in erp5 catalog view in
restricted environment.

Required as preparation for migrating to ERP5 catalog which itself has an accessor
`isIndexable` which we plan to remain disabled all the time. So better to rely
directly on isIndexable from erp5 site object.

This would also remove the need of evaluating the globals every time.

/reviewed-on nexedi/erp5!314

/reviewed-on nexedi/erp5!313