Fixes: #3815
Verified on my very large documents that it doesn't unnecessarily and
unsuccessfully "repair" them.
You may or may not wish to keep the version check in `patch_psparser`.
Since ~you're pinning the version of pdfminer.six and since it isn't
guaranteed that the bug in question will be fixed in the next
pdfminer.six release (but it is rather serious, so I should hope so),
then perhaps you just want to unconditionally patch it.~ it seems like
pinning of versions is only operative when running from Docker (good!)
so never mind! Keep that version check!
Also corrected an import so that if you do feel like using a newer
version of pdfminer.six, it won't break on you.
---------
Authored-by: David Huggins-Daines <dhdaines@logisphere.ca>
This PR refactors the data structure for `list[LayoutElement]` and
`list[TextRegion]` used in partition pdf/image files.
- new data structure replaces a list of objects with one object with
`numpy` array to store data
- this only affects partition internal steps and it doesn't change input
or output signature of `partition` function itself, i.e., `partition`
still returns `list[Element]`
- internally `list[LayoutElement]` -> `LayoutElements`;
`list[TextRegion]` -> `TextRegions`
- current refactor stops before clean up pdfminer elements inside
inferred layout elements -> the algorithm of clean up needs to be
refactored before the data structure refactor can move forward. So
current refactor converts the array data structure into list data
structure with `element_array.as_list()` call. This is the last step
before turning `list[LayoutElement]` into `list[Element]` as return
- a future PR will update this last step so that we build
`list[Element]` from `LayoutElements` data structure instead.
The goal of this PR is to replace the data structure as much as possible
without changing underlying logic. There are a few places where the
slicing or filtering logic was simple enough to be converted into vector
data structure operations. Those are refactored to be vector based. As a
result there is some small improvements observed in ingest test. This is
likely because the vector operations cleaned up some previous
inconsistency in data types and operations.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
This change adds the ability to filter out characters predicted by
Tesseract with low confidence scores.
Some notes:
- I intentionally disabled it by default; I think some low score(like
0.9-0.95 for Tesseract) could be a safe choice though
- I wanted to use character bboxes and combine them into word bbox
later. However, a bug in Tesseract in some specific scenarios returns
incorrect character bboxes (unit tests caught it 🥳 ). More in comment in
the code
This PR aims to add support for link extraction in pdf `hi_res`
strategy. The `partition_pdf()` function now supports link extraction
when using the `hi_res` strategy, allowing users to extract hyperlinks
from PDF documents.
### Summary
- Added functionalities to support link extraction in hi_res flow
- Enhanced word extraction functionality used for link extraction in
both `fast` and `hi_res` flows, resulted in more correct `start_index`
and `text` in `links` metadata.
- Updated ingest fixture update workflow to not skip Astra DB source
test
### Testing
```
elements = partition_pdf(
filename="example-docs/pdf/embedded-link.pdf",
strategy="hi_res"
)
assert len(elements[0].metadata.links) == 3
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
Co-authored-by: cragwolfe <crag@unstructured.io>
This PR bumps `unstructured-inference` to `0.8.0`, which introduces
vectorized data structure for layout elements and text regions.
This PR also cleans up a few places in CI that has repeated definition
of env variables or missing installation of testing dependencies in
cache.
A few document ingest results are changed:
- two places for `biomed-api` (actually processed locally on runner) are
due to very small changes in numerical results of the bounding box
areas: one results in a duplicated page number/header and another
results in a deduplication of a word of a sentence that starts in a new
line. (yes, two cases goes in opposite directions)
- the layout parser paper now outputs the code lines with page number
inside the code box as list items
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
**Summary**
Remove unused `include_metadata` parameter.
**Additional Context**
- The `include_metadata` parameter was originally added circa v0.7.12 as
a mechanism for avoiding the "double-decorating" problem on delegating
partitioners.
- It turns out it doesn't fully address that problem, is now unused, and
is unnecessary for the solution we'll be adding as part of pluggable
partitioners.
- Remove the unnecessary complexity introduced by this unused parameter.
**Summary**
In preparation for pluggable auto-partitioners simplify metadata as
discussed.
**Additional Context**
- Pluggable auto-partitioners requires partitioners to have a consistent
call signature. An arbitrary partitioner provided at runtime needs to
have a call signature that is known and consistent. Basically
`partition_x(filename, *, file, **kwargs)`.
- The current `auto.partition()` is highly coupled to each distinct
file-type partitioner, deciding which arguments to forward to each.
- This is driven by the existence of "delegating" partitioners, those
that convert their file-type and then call a second partitioner to do
the actual partitioning. Both the delegating and proxy partitioners are
decorated with metadata-post-processing decorators and those decorators
are not idempotent. We call the situation where those decorators would
run twice "double-decorating". For example, EPUB converts to HTML and
calls `partition_html()` and both `partition_epub()` and
`partition_html()` are decorated.
- The way double-decorating has been avoided in the past is to avoid
sending the arguments the metadata decorators are sensitive to to the
proxy partitioner. This is very obscure, complex to reason about,
error-prone, and just overall not a viable strategy. The better solution
is to not decorate delegating partitioners and let the proxy partitioner
handle all the metadata.
- This first step in preparation for that is part of simplifying the
metadata processing by removing unused or unwanted legacy parameters.
- `date_from_file_object` is a misnomer because a file-object never
contains last-modified data.
- It can never produce useful results in the API where last-modified
information must be provided by `metadata_last_modified`.
- It is an undocumented parameter so not in use.
- Using it can produce incorrect metadata.
This PR implements splitting of `pdfminer` elements (`groups of text
chunks`) into smaller bounding boxes (`text lines`). This implementation
prevents loss of information from the object detection model and
facilitates more effective removal of duplicated `pdfminer` text. This
PR also addresses #3430.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
This PR changes the way the analysis tools can be used:
- by default if `analysis` is set to `True` in `partition_pdf` and the
strategy is resolved to `hi_res`:
- for each file 4 layout dumps are produced and saved as JSON files
(`object_detection`, `extracted`, `ocr`, `final`) - similar way to the
current `object_detection` dump
- the drawing functions/classes now accept these dumps accordingly
instead of the internal classes instances (like `TextRegion`,
`DocumentLayout`
- it makes it possible to use the lightweight JSON files to render the
bboxes of a given file after the partition is done
- `_partition_pdf_or_image_local` has been refactored and most of the
analysis code is now encapsulated in `save_analysis_artifiacts` function
- to do this, helper function `render_bboxes_for_file` is added
<img width="338" alt="Screenshot 2024-08-28 at 14 37 56"
src="https://github.com/user-attachments/assets/10b6fbbd-7824-448d-8c11-52fc1b1b0dd0">
This PR reverts `pytesseract` dependency to `unstructured.pytesseract`
fork due to the unavailability of some recent release versions of
`pytesseract` on PyPI.
This PR also addresses an issue encountered during the publication of
`unstructured==0.15.4` to PyPI. The error was due to the fact that PyPI
does not allow direct dependencies from Version Control System URLs like
GitHub in the `install_requires` or `extras_require` sections of the
`setup.py` file.
# Description:
Passing `max_pages` argument allows rejecting pdf files which exceeds
this page number limit while `high_res` strategy is chosen. By default
it will allow parsing pdf files with unlimited number of pages.
# Testing:
```python
from unstructured.partition.auto import partition
elements = partition(filename="unstructured/example-docs/pdf/reliance.pdf", strategy='hi_res') # should pass
elements = partition(filename="unstructured/example-docs/pdf/reliance.pdf", strategy='hi_res', max_pages=4) # should pass
elements = partition(filename="unstructured/example-docs/pdf/reliance.pdf", strategy='hi_res', max_pages=2) # should raise PdfMaxPagesExceededError
```
This PR aims to improve the organization and readability of our example
documents used in unit tests, specifically focusing on PDF and image
files.
### Summary
- Created two new subdirectories in the `example-docs` folder:
- `pdf/`: for all PDF example files
- `img/`: for all image example files
- Moved relevant PDF files from `example-docs/` to `example-docs/pdf/`
- Moved relevant image files from `example-docs/` to `example-docs/img/`
- Updated file paths in affected unit & ingest tests to reflect the new
directory structure
### Testing
All unit & ingest tests should be updated and verified to work with the
new file structure.
## Notes
Other file types (e.g., office documents, HTML files) remain in the root
of `example-docs/` for now.
## Next Steps
Consider similar reorganization for other file types if this structure
proves to be beneficial.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
**Summary**
In preparation for further work on auto-partitioning (`partition()`),
improve typing and organize `test_auto.py` by introducing categories.
This PR adds new capabilities for drawing bboxes for each layout
(extracted, inferred, ocr and final) + OD model output dump as a json
file for better analysis.
---------
Co-authored-by: Christine Straub <christinemstraub@gmail.com>
Co-authored-by: Michal Martyniak <michal.martyniak@deepsense.ai>
The Issue:
When extracting images from pdfs, we use the metadata page number to
index into a list of the images. However, the metadata page number can
now be changed via `starting_page_number`. To get the true page index,
we need to subtract this value.
Testing:
Run this snippet in a python shell. Before the fix, this throws an
IndexError. On this branch, it will return the elements.
```
from unstructured.partition.auto import partition
filename = "example-docs/layout-parser-paper-with-table.pdf"
partition(filename, strategy="hi_res", extract_image_block_types=["Image", "Table"], starting_page_number=20)
```
---------
Co-authored-by: Matt Robinson <mrobinson@unstructuredai.io>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
### Summary
- bump unstructured-inference to `0.7.35` which fixed syntax for
generated HTML tables
- update unit tests and ingest test fixtures to reflect changes in the
generated HTML tables
- cut a release for `0.14.6`
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
This PR aims to pass `kwargs` through `fast` strategy pipeline, which
was missing as part of the previous PR -
https://github.com/Unstructured-IO/unstructured/pull/3030.
I also did some code refactoring in this PR, so I recommend reviewing
this PR commit by commit.
### Summary
- pass `kwargs` through `fast` strategy pipeline, which will allow users
to specify additional params like `sort_mode`
- refactor: code reorganization
- cut a release for `0.14.0`
### Testing
CI should pass
This PR aims to skip element sorting when determining whether embedded
text can be extracted. The extracted elements in this step are returned
as final elements only for the `fast` strategy pipeline and are never
used for other strategy pipelines (`hi_res`, `ocr`).
Removing element sorting in this step and adding it to the `fast`
strategy pipeline later will improve performance and reduce execution
time.
### Summary
- skip element sorting when determining whether embedded text can be
extracted.
- add `_partition_pdf_with_pdfparser()` function for fast` strategy
pipeline
### Testing
CI should pass.
This PR attempts to fix a memory issue, which resulted in errors like
this: https://github.com/Unstructured-IO/unstructured/issues/2931
The root cause seems to be in how ListItems are being combined, not in
how hashes or parent IDs are updated.
When `assign_and_map_hash_ids()` is called and elements (or elements'
metadata) do not have unique memory addresses, then updating the
parent_id of one element will also overwrite the parent_id of some other
element.
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Part two of: https://github.com/Unstructured-IO/unstructured/pull/2842
Main changes compared to part one:
* hash computation includes element's sequence number on page, page
number, document filename and its text
* there are more test for deterministic behavior of IDs returned by
partitioning functions + their uniqueness (guaranteed at the document
level, and high probability across multiple documents)
This PR addresses the following issue:
https://github.com/Unstructured-IO/unstructured/issues/2461
Introduce `date_from_file_object` to `partition*` functions, by default
set to `False`.
If set to `True` and file is provided via `file` parameter, partition
will attempt to infer last modified date from `file`'s contents
otherwise last modified metadata will be set to `None`.
---------
Co-authored-by: Filip Knefel <filip@unstructured.io>
Co-authored-by: Ronny H <138828701+ron-unstructured@users.noreply.github.com>
### Summary
Detects headers and footers when using `partition_pdf` with the fast
strategy. Identifies elements that are positioned in the top or bottom
5% of the page as headers or footers. If no coordinate information is
available, an element won't be detected as a header or footer.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: MthwRobinson <MthwRobinson@users.noreply.github.com>
This PR is the last in a series of PRs for refactoring and fixing the
language parameters (`languages` and `ocr_languages` so we can address
incorrect input by users. See #2293
It is recommended to go though this PR commit-by-commit and note the
commit message. The most significant commit is "update
check_languages..."
- there are multiple places setting the default `hi_res_model_name` in
both `unstructured` and `unstructured-inference`
- they lead to inconsistency and unexpected behaviors
- this fix removes a helper in `unstructured` that tries to set the
default hi_res layout detection model; instead we rely on the
`unstructured-inference` to provide that default when no explicit model
name is passed in
## test
```bash
UNSTRUCTURED_INCLUDE_DEBUG_METADATA=true ipython
```
```python
from unstructured.partition.auto import partition
# find a pdf file
elements = partition("foo.pdf", strategy="hi_res")
assert elements[0].metadata.detection_origin == "yolox"
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
### Summary
The goal of this PR is to keep all image elements when using "hi_res"
strategy. Previously, `Image` elements with small chunks of text were
ignored unless the image block extraction parameters
(`extract_images_in_pdf` or `extract_image_block_types`) were specified.
Now, all image elements are kept regardless of whether the image block
extraction parameters are specified.
### Testing
- on `main` branch,
```
elements = partition_pdf(
filename="example-docs/embedded-images.pdf",
strategy="hi_res",
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print("number of image elements: ", len(image_elements))
```
The above code will display `number of image elements: 0`.
- on this `feature` branch,
The same code will display `number of image elements: 3`
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
Currently, we're using different kwarg names in partition() and
partition_pdf(), which has implications for the API since it goes
through partition().
### Summary
- rename `extract_element_types` -> `extract_image_block_types`
- rename `image_output_dir_path` to `extract_image_block_output_dir`
- rename `extract_to_payload` -> `extract_image_block_to_payload`
- rename `pdf_extract_images` -> `extract_images_in_pdf` in
`partition.auto`
- add unit tests to test element extraction for `pdf/image` via
`partition.auto`
### Testing
CI should pass.
Closes#2323.
### Summary
- update logic to return "hi_res" if either `extract_images_in_pdf` or
`extract_element_types` is set
- refactor: remove unused `file` parameter from
`determine_pdf_or_image_strategy()`
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
extract_element_types=["Image"],
extract_to_payload=True,
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print(image_elements)
```
Closes#2302.
### Summary
- add functionality to get a Base64 encoded string from a PIL image
- store base64 encoded image data in two metadata fields: `image_base64`
and `image_mime_type`
- update the "image element filter" logic to keep all image elements in
the output if a user specifies image extraction
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
extract_element_types=["Image", "Table"],
extract_to_payload=True,
)
```
or
```
from unstructured.partition.auto import partition
elements = partition(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
pdf_extract_element_types=["Image", "Table"],
pdf_extract_to_payload=True,
)
```
Closes#2160
Explicitly adds `hi_res_model_name` as kwarg to relevant functions and
notes that `model_name` is to be deprecated.
Testing:
```
from unstructured.partition.auto import partition
filename = "example-docs/DA-1p.pdf"
elements = partition(filename, strategy="hi_res", hi_res_model_name="yolox")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Steve Canny <stcanny@gmail.com>
Co-authored-by: Christine Straub <christinemstraub@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
### Summary
This PR is the second part of `pdfminer` refactor to move it from
`unstructured-inference` repo to `unstructured` repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/294. This
PR adds logic to merge the extracted layout with the inferred layout.
The updated workflow for the `hi_res` strategy:
* pass the document (as data/filename) to the `inference` repo to get
`inferred_layout` (DocumentLayout)
* pass the `inferred_layout` returned from the `inference` repo and the
document (as data/filename) to the `pdfminer_processing` module, which
first opens the document (create temp file/dir as needed), and splits
the document by pages
* if is_image is `True`, return the passed
inferred_layout(DocumentLayout)
* if is_image is `False`:
* get extracted_layout (TextRegions) from the passed
document(data/filename) by pdfminer
* merge `extracted_layout` (TextRegions) with the passed
`inferred_layout` (DocumentLayout)
* return the `inferred_layout `(DocumentLayout) with updated elements
(all merged LayoutElements) as merged_layout (DocumentLayout)
* pass merged_layout and the document (as data/filename) to the `OCR`
module, which first opens the document (create temp file/dir as needed),
and splits the document by pages (convert PDF pages to image pages for
PDF file)
### Note
This PR also fixes issue #2164 by using functionality similar to the one
implemented in the `fast` strategy workflow when extracting elements by
`pdfminer`.
### TODO
* image extraction refactor to move it from `unstructured-inference`
repo to `unstructured` repo
* improving natural reading order by applying the current default
`xycut` sorting to the elements extracted by `pdfminer`
### Summary
Add a procedure to repair PDF when the PDF structure is invalid for
`PDFminer` to process.
This PR handles two cases of `PSSyntaxError Invalid dictionary
construct: ...`:
* PDFminer open entire document and create pages generator on
`PDFPage.get_pages(fp)`: [sentry log
example](https://unstructuredio.sentry.io/issues/4655715023/?alert_rule_id=14681339&alert_type=issue¬ification_uuid=d8db4cf4-686f-4504-8a22-74a79a8e966f&project=4505909127086080&referrer=slack)
* PDFminer's interpreter process a single page on
`interpreter.process_page(page)`: [sentry log
example](https://unstructuredio.sentry.io/issues/4655898781/?referrer=slack¬ification_uuid=0d929d48-f490-4db8-8dad-5d431c8460bc&alert_rule_id=14681339&alert_type=issue)
**Additional tech details:**
* Add new dependency `pikepdf` in `requirements/extra-pdf-image.in`,
which is used for repairing PDF.
* Add new denpendenct `pypdf` in `requirements/extra-pdf-image.in`,
which is used to find the error page from entire document by reading the
PDF file again (can't find a way to split pdf in PDFminer).
* Refactor the `is null` check for `get_uris_from_annots`, since the
root cause is that `get_uris` passed a None `annots` to
`get_uris_from_annots`, so the Null check should happen in `get_uris`.
* Add more type protection in `get_uris_from_annots` when using any
`PDFObjRef.resolve()` as `dict` (it could still be a `PDFObjRef`). This
should fix :
* https://github.com/Unstructured-IO/unstructured/issues/1922 where
`annotation_dict` is a `PDFObjRef`
* https://github.com/Unstructured-IO/unstructured/issues/1921 where
`rect` is a `PDFObjRef`
### Test
Added three test files (both are larger than 500 KB) for unittests to
test:
* Repair entire doc
* Repair one page
* Reprocess failure after repairing one page (just return the elements
before error page in this case).
* Also seems like splitting the document into smaller pages could fix
this problem, but not sure why. For example, I saw error from reprocess
in the whole
[cancer.pdf](https://github.com/Unstructured-IO/unstructured/files/13461616/cancer.pdf)
doc, but no error when i split the pdf by error page....
* tested if i can repair the entire doc again in this case, saw other
error which means repairing is not helping imo
* PDFminer can process the whole doc after pikepdf only repaired the
entire doc in the first place, but we can't repair by pages in this way
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Closes#2059.
We've found some pdfs that throw an error in pdfminer. These files use a
ICCBased color profile but do not include an expected value `N`. As a
workaround, we can wrap pdfminer and drop any colorspace info, since we
don't need to render the document.
To verify, try to partition the document in the linked issue.
```
elements = partition(filename="google-2023-environmental-report_condensed.pdf", strategy="fast")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Closes#2038.
### Summary
The `fast` strategy should not fall back to a more expensive strategy.
### Testing
For
[9493801-p17.pdf](https://github.com/Unstructured-IO/unstructured/files/13292884/9493801-p17.pdf),
the following code should return an empty list.
```
elements = partition(filename=filename, strategy="fast")
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
### Summary
Closes#2011
`languages` was missing from the metadata when partitioning pdfs via
`hi_res` and `fast` strategies and missing from image partitions via
`hi_res`. This PR adds `languages` to the relevant function calls so it
is included in the resulting elements.
### Testing
On the main branch, `partition_image` will include `languages` when
`strategy='ocr_only'`, but not when `strategy='hi_res'`:
```
filename = "example-docs/english-and-korean.png"
from unstructured.partition.image import partition_image
elements = partition_image(filename, strategy="ocr_only", languages=['eng', 'kor'])
elements[0].metadata.languages
elements = partition_image(filename, strategy="hi_res", languages=['eng', 'kor'])
elements[0].metadata.languages
```
For `partition_pdf`, `'ocr_only'` will include `languages` in the
metadata, but `'fast'` and `'hi_res'` will not.
```
filename = "example-docs/korean-text-with-tables.pdf"
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(filename, strategy="ocr_only", languages=['kor'])
elements[0].metadata.languages
elements = partition_pdf(filename, strategy="fast", languages=['kor'])
elements[0].metadata.languages
elements = partition_pdf(filename, strategy="hi_res", languages=['kor'])
elements[0].metadata.languages
```
On this branch, `languages` is included in the metadata regardless of
strategy
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Coniferish <Coniferish@users.noreply.github.com>
Closes#2027
Tables or pages that contain only numbers are returned as floats in a
pandas.DataFrame when the image or page is converted from
`.image_to_data()`. An AttributeError was raised downstream when trying
to `.strip()` the floats. This update converts those floats if needed
and otherwise strips the text.
Testing (note: the document used for testing is new, so you will have to
copy it to the main branch in order to see that this snippet raises an
AttributeError on the main branch, but works on this branch)
```
from unstructured.partition.pdf import partition_pdf
filename = "example-docs/all-number-table.pdf"
partition_pdf(filename, strategy="ocr_only")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Fix TypeError: string indices must be integers. The `annotation_dict`
variable is conditioned to be `None` if instance type is not dict. Then
we add logic to skip the attempt if the value is `None`.
### Summary
Update `ocr_only` strategy in `partition_pdf()`. This PR adds the
functionality to get accurate coordinate data when partitioning PDFs and
Images with the `ocr_only` strategy.
- Add functionality to perform OCR region grouping based on the OCR text
taken from `pytesseract.image_to_string()`
- Add functionality to get layout elements from OCR regions (ocr_layout)
for both `tesseract` and `paddle`
- Add functionality to determine the `source` of merged text regions
when merging text regions in `merge_text_regions()`
- Merge multiple test functions related to "ocr_only" strategy into
`test_partition_pdf_with_ocr_only_strategy()`
- This PR also fixes [issue
#1792](https://github.com/Unstructured-IO/unstructured/issues/1792)
### Evaluation
```
# Image
PYTHONPATH=. python examples/custom-layout-order/evaluate_natural_reading_order.py example-docs/double-column-A.jpg ocr_only xy-cut image
# PDF
PYTHONPATH=. python examples/custom-layout-order/evaluate_natural_reading_order.py example-docs/multi-column-2p.pdf ocr_only xy-cut pdf
```
### Test
- **Before update**
All elements have the same coordinate data
- **After update**
All elements have accurate coordinate data
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
