| # pip install mermaid-python | |
| # https://pypi.org/project/mermaid-python/ | |
| from mermaid import Mermaid | |
| from langraph.graph import StateGraph, START, END | |
| class State(TypedDict): | |
| message: str | |
| def node_1(state): |
| Function FormatTemplate(template As String, ParamArray values() As Variant) As String | |
| Dim result As String | |
| Dim i As Integer | |
| result = template | |
| For i = LBound(values) To UBound(values) | |
| If InStr(template, "{" & i & ":") > 0 Then | |
| Dim formatSpecifier As String |
Link to the webinar: https://www.crowdcast.io/e/interactive
| <form method="post"> | |
| {{ form.hidden_tag() }} | |
| {{ form.example }} | |
| <input type="submit"> | |
| </form> |
| # pdfTextMiner.py | |
| # Python 2.7.6 | |
| # For Python 3.x use pdfminer3k module | |
| # This link has useful information on components of the program | |
| # https://euske.github.io/pdfminer/programming.html | |
| # http://denis.papathanasiou.org/posts/2010.08.04.post.html | |
| ''' Important classes to remember | |
| PDFParser - fetches data from pdf file |
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs
