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Chore (refactor): support table extraction with pre-computed ocr data (#1801 )

### Summary

Table OCR refactor, move the OCR part for table model in inference repo
to unst repo.
* Before this PR, table model extracts OCR tokens with texts and
bounding box and fills the tokens to the table structure in inference
repo. This means we need to do an additional OCR for tables.
* After this PR, we use the OCR data from entire page OCR and pass the
OCR tokens to inference repo, which means we only do one OCR for the
entire document.

**Tech details:**
* Combined env `ENTIRE_PAGE_OCR` and `TABLE_OCR` to `OCR_AGENT`, this
means we use the same OCR agent for entire page and tables since we only
do one OCR.
* Bump inference repo to `0.7.9`, which allow table model in inference
to use pre-computed OCR data from unst repo. Please check in
[PR](https://github.com/Unstructured-IO/unstructured-inference/pull/256).
* All notebooks lint are made by `make tidy`
* This PR also fixes
[issue](https://github.com/Unstructured-IO/unstructured/issues/1564),
I've added test for the issue in
`test_pdf.py::test_partition_pdf_hi_table_extraction_with_languages`
* Add same scaling logic to image [similar to previous Table
OCR](https://github.com/Unstructured-IO/unstructured-inference/blob/main/unstructured_inference/models/tables.py#L109C1-L113),
but now scaling is applied to entire image

### Test
* Not much to manually testing expect table extraction still works
* But due to change on scaling and use pre-computed OCR data from entire
page, there are some slight (better) changes on table output, here is an
comparison on test outputs i found from the same test
`test_partition_image_with_table_extraction`:

screen shot for table in `layout-parser-paper-with-table.jpg`:
<img width="343" alt="expected"
src="https://github.com/Unstructured-IO/unstructured/assets/63475068/278d7665-d212-433d-9a05-872c4502725c">
before refactor:
<img width="709" alt="before"
src="https://github.com/Unstructured-IO/unstructured/assets/63475068/347fbc3b-f52b-45b5-97e9-6f633eaa0d5e">
after refactor:
<img width="705" alt="after"
src="https://github.com/Unstructured-IO/unstructured/assets/63475068/b3cbd809-cf67-4e75-945a-5cbd06b33b2d">

### TODO
(added as a ticket) Still have some clean up to do in inference repo
since now unst repo have duplicate logic, but can keep them as a fall
back plan. If we want to remove anything OCR related in inference, here
are items that is deprecated and can be removed:
*
[`get_tokens`](https://github.com/Unstructured-IO/unstructured-inference/blob/main/unstructured_inference/models/tables.py#L77)
(already noted in code)
* parameter `extract_tables` in inference
*
[`interpret_table_block`](https://github.com/Unstructured-IO/unstructured-inference/blob/main/unstructured_inference/inference/layoutelement.py#L88)
*
[`load_agent`](https://github.com/Unstructured-IO/unstructured-inference/blob/main/unstructured_inference/models/tables.py#L197)
* env `TABLE_OCR` 

### Note
if we want to fallback for an additional table OCR (may need this for
using paddle for table), we need to:
* pass `infer_table_structure` to inference with `extract_tables`
parameter
* stop passing `infer_table_structure` to `ocr.py`

---------

Co-authored-by: Yao You <yao@unstructured.io>

2023-10-21 00:24:23 +00:00

load-into-mysql.ipynb

Chore (refactor): support table extraction with pre-computed ocr data (#1801 )

2023-10-21 00:24:23 +00:00

README.md

docs: unstructured -> MySQL example (#557 )

2023-05-09 13:26:49 +00:00

requirements.txt

docs: unstructured -> MySQL example (#557 )

2023-05-09 13:26:49 +00:00

README.md

Loading `unstructured` outputs into MySQL

The following example shows how to load unstructured output into MySQL. This allows you to run queries based on metadata that the unstructured library has extracted. Follow the instructions here to install MySQL on your system. If you're using homebrew on Mac, you can install MySQL with brew install mysql.

Once you have installed MySQL, you can connect to MySQL with the command mysql -u root. You can create a non-root user and an unstructured_example database using the following commands:

CREATE USER '<username>'@'localhost' IDENTIFIED BY '<password>';
CREATE DATABASE unstructured_example;
GRANT ALL PRIVILEGES ON unstructured_example.* TO '<username>'@'localhost';

Running the example

Run pip install -r requirements.txt to install the Python dependencies.
Run `jupyter-notebook to start.
Run the load-into-mysql.ipynb notebook.

README.md

Loading unstructured outputs into MySQL

Running the example

Loading `unstructured` outputs into MySQL