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Python for Supernote - installation & features overview
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*** Python 3.13.0 does not work. Use instead Python 3.12.3 ***
Also: the archiving is now done in an "archive" subfolder (not anymore in the "old" subfolder)
Links to jump to PySN key features and chapters covered:
0:00 Features
2:15 Installation
10:20 Bulk export and Backup
12:55 Bulk export and external links between pdf converted notebooks
13:55 Dictionaries to correct misidentified words by the recognition engine
15:30 Handwritten notes searches in pdf
16:00 PDF Table of Contents based on headers (Text bookmarks)
16:20 PDF Table of Contents based on headers (Graphic links)
17:05 Markdown file from recognized text
17:47 Html file from recognized text
19:15 Keyword settings
23:50 Word docx from recognized text, using headers style and dictionaries
26:35 PDF annotation on Supernote
29:20 PySN annotation differences
30:30 PySN optional text recognition using Microsoft Computer Vision
31:55 Examples of "Digest" using PySN
39:15 Table of contents for stars
39:34 Metadata stored: Keywords, author, original location of notebook on Supernote, custom meta
40:00 The Big Picture: comments on e-ink hardware + software and the near future
40:45 Mining embedded text in PDF export using Adobe Acrobat AI module
41:55 Conclusion
Also: the archiving is now done in an "archive" subfolder (not anymore in the "old" subfolder)
Links to jump to PySN key features and chapters covered:
0:00 Features
2:15 Installation
10:20 Bulk export and Backup
12:55 Bulk export and external links between pdf converted notebooks
13:55 Dictionaries to correct misidentified words by the recognition engine
15:30 Handwritten notes searches in pdf
16:00 PDF Table of Contents based on headers (Text bookmarks)
16:20 PDF Table of Contents based on headers (Graphic links)
17:05 Markdown file from recognized text
17:47 Html file from recognized text
19:15 Keyword settings
23:50 Word docx from recognized text, using headers style and dictionaries
26:35 PDF annotation on Supernote
29:20 PySN annotation differences
30:30 PySN optional text recognition using Microsoft Computer Vision
31:55 Examples of "Digest" using PySN
39:15 Table of contents for stars
39:34 Metadata stored: Keywords, author, original location of notebook on Supernote, custom meta
40:00 The Big Picture: comments on e-ink hardware + software and the near future
40:45 Mining embedded text in PDF export using Adobe Acrobat AI module
41:55 Conclusion
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