I am working with data from social media. I am currently looking for a (free?) web-speech2normal-speech dictionary.
A dictionary that contains mapping likes
ur -> you are
Does anyone have any knowledge about such a freely available resource?
Related
Project Environment
The environment we are currently developing is using Windows 10. nodejs 10.16.0, express web framework. The actual environment being deployed is the Linux Ubuntu server and the rest is the same.
What technology do you want to implement?
The technology that I want to implement is the information that I entered when I joined the membership. For example, I want to automatically put it in the input text box using my name, age, address, phone number, etc. so that the user only needs to fill in the remaining information in the PDF. (PDF is on some of the webpages.)
If all the information is entered, the PDF is saved and the document is sent to another vendor, which is the end.
Current Problems
We looked at about four days for PDFs, and we tried to create PDFs when we implemented the outline, structure, and code, just like it was on this site at https://web.archive.org/web/20141010035745/http://gnupdf.org/Introduction_to_PDF
However, most PDFs seem to be compressed into flatDecode rather than this simple. So I also looked at Data extraction from /Filter /FlateDecode PDF stream in PHP and tried to decompress it using QPDF.
Unzip it for now.Well, I thought it would be easy to find out the difference compared to the PDF without Kim after putting it in the first name.
However, there is too much difference even though only three characters are added... And the PDF structure itself is more difficult and complex to proceed with.
Note : https://www.adobe.com/content/dam/acom/en/devnet/pdf/pdfs/PDF32000_2008.pdf (PDF official document in English)
Is there a way to solve the problem now?
It sounds like you want to create a PDF from scratch and possibly extract data from it and you are finding this a more difficult prospect than you first imagined.
Check out my answer here on why PDF creation and reading is non-trivial and why you should reach for a tool you help you do this:
https://stackoverflow.com/a/53357682/1669243
I'm trying to extract some entries from a PDF, but the bad formatting is making it inconvenient to simply parse through like a normal document. There isn't any consistent positioning for the text, so each entry is a unique scramble with no consistent pattern I can find. I only want the entry name and the info on the right, not the field name or description.
I've tried experimenting with headers and layout info using the PyPDF2 Module but there doesn't seem to be any metadata for the PDF besides basic author info.
My idea was using the Google Cloud Vision API to transcribe the text, but that brings up issues of auto-positioning.
Does anyone know of a better methodology for this, or if not, simply how to execute the positioning for the Cloud Vision API?
I am trying to implement a document management system using Sharepoint. One major issue is that colleagues cannot find documents in the current setup (local fileserver). They have asked that we have a system that scans uploaded documents and automatically looks for keywords in them and then populates a "Meta" column.
I have had sort of success with OCR on image files, but getting keywords out of office documents (doc, xls etc.) I have had no success until now.
Is there a way to setup a flow to do this task for me?
any help is much aprechiated.
i tried "Get file metadata" and Azure "Text analysis", but it seems to take the raw data of the files (XML I assume) and returns that the document is to large to analyse.
There is something vague about this requirement - how is a keyword defined in a document?
Therefore, first obvious solution would be to assign keywords for each file upon uploading it. You may create a process for this with flow - have tasks, reminders and so on.
Automating this with OCR first means that you need to user OCR that works with MS flow you have only one choice - ElasticOCR. Then, in your flow
- feed the document content to the ElasticOCR action
- keep in mind that OCR is not 100% accurate
- analyze the generated text content according to your keyword definition
- finally write the meta back to the library in the corresponding columns.
Having worked on a similar requirement, we asked uploaders to publish their documents with a short abstract(column from the content type). The assumption is the abstract contains the keywords and is stored in a multi-line column - making it searchable site wide.
I am trying to search for keywords contained in the metadata of a PDF doc. I am unsure if this is possible. Any guidance would be much appreciated!
Here is an example of the keywords/tags in a PDF I am referring to
I know it is possible to add fields to the search index, but am unsure how to map it. I have tried the following but it did not work.
Here is how the keywords metadata would work -
Adding a keywords (metadata) to the pdf file would not work as only selected custom metadata tags are supported for pdf.
Refer this document - https://learn.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage
A work around to this problem could be add metadata tag to the pdf file blob itself.
After we create a index in azure search for ("All Metadata"/Storage Metadata) this key starts appearing under the list of field names to select(search/retrieve/filter etc.).
And finally we can search on the custom keywords now.
The Keywords tag is not one of the ones we support through the metadata_ format (the ones that are, are listed here). If you add a field to the index called "Keywords", does it extract it? Also, I if you look at the properties of the PDF in something like Azure Storage Explorer, I assume this keyword metadata is still there and it is called "Keywords". If not, this might give some additional insight.
I'm new to this forum and to Orange.
I don't really now Python at this point but am ready to learn.
However, before going further in this environment I would like to know if it can answer my needs !
What I am basically doing is "transforming" PDF product catalogues into Excel files that can be used by another software to create a database for another software.
I have tiles catalogues in PDF just like this one :
and turn it into this type of xls table : http://imgur.com/BtLBkOS
I basically need it to retrieve the article number, the colour, the size (e.g: 20x20). The G/B parts are completed manually after it has been done.
All catalogues are not the same so I sorted out some using pdftotext, RegEx with Notepad++
But I would like to know if this data mining solution could work it out ?
Orange does not support reading PDF files. You will have to use specialized utilities or program it yourself.