### Summary
In order to convert between incompatible language codes from packages
used for OCR, this change adds a function to map between any standard
language codes and tesseract OCR specific codes. Users can input
language information to `languages` in any Tesseract-supported langcode
or any ISO 639 standard language code.
### Details
- Introduces the
[python-iso639](https://pypi.org/project/python-iso639/) package for
matching standard language codes. Recompiles all dependencies.
- If a language is not already supplied by the user as a Tesseract
specific langcode, supplies all possible script/orthography variants of
the language to the Tesseract OCR agent.
### Test
Added many unit tests for a variety of language combinations, special
cases, and variants. For general testing, call partition functions with
any lang codes in the languages parameter (Tesseract or standard).
for example,
```
from unstructured.partition.auto import partition
elements = partition(filename="example-docs/layout-parser-paper.pdf", strategy="hi_res", languages=["en", "chi"])
print("\n\n".join([str(el) for el in elements]))
```
should supply eng+chi_sim+chi_sim_vert+chi_tra+chi_tra_vert to Tesseract
This bump removes the preprocessing before table structure extraction
and improves the OCR results for tables.
---------
Co-authored-by: yuming-long <yuming-long@users.noreply.github.com>
### Summary
Duplicate PR of #1259 because of issues with checks
Closes#1227, which found that `nan` values were present in the
coordinates being generated for some elements.
This breaks logic out from `add_pytesseract_bbox_to_elements` to new
functions `_get_element_box` and
`convert_multiple_coordinates_to_new_system`. It also updates the logic
to check that the current bounding box matches the first character of
the element's text (as to avoid the `~` characters that
`pytesseract.image_to_boxes` includes, but are not present in
`pytesseract.image_to_string`.
### Testing
```
from unstructured.partition.image import partition_image
from PIL import Image, ImageDraw
filename="example-docs/layout-parser-paper-with-table.jpg"
elements = partition_image(filename=filename, strategy="ocr_only")
image = Image.open(filename)
draw = ImageDraw.Draw(image)
for i, element in enumerate(elements):
print(i, element.metadata.coordinates)
if element.metadata.coordinates:
draw.polygon(element.metadata.coordinates.points, outline="red", width=2)
output = "example-docs/box-layout-parser-paper-with-table.jpg"
image.save(output)
image.close()
```
---------
Co-authored-by: qued <64741807+qued@users.noreply.github.com>
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
`partition_pdf` allows for passing a `model_name` parameter. Given the
similarity between the image and PDF pipelines, the expected behavior is
that `partition_image` should support the same parameter, but
`partition_image` was unintentionally not passing along its `kwargs`.
This was corrected by adding the kwargs to the downstream call.
#### Testing:
```python
from unstructured.partition.image import partition_image
output1 = partition_image("example-docs/layout-parser-paper-fast.jpg", model_name="detectron2_onnx")
output2 = partition_image("example-docs/layout-parser-paper-fast.jpg", model_name="yolox")
# These shouldn't be the same, since they were produced using different models.
assert output1 != output2
```
The assertion should fail on `main`, but pass on this branch.
### Summary
Partial solution to #1185.
Related to #1222.
Creates decorator from `chunk_by_title` cleaning brick.
Breaks a document into sections based on the presence of Title elements.
Also starts a new section under the following conditions:
- If metadata changes, indicating a change in section or page or a
switch to processing attachments. If `multipage_sections=True`, sections
can span pages. `multipage_sections` defaults to True.
- If the length of the section exceeds `new_after_n_chars` characters.
The default is 1500. The **chunking function does not split individual
elements**, so it's possible for a section to exceed that threshold if
an individual element if over `new_after_n_chars characters`, which
could occur with a long NarrativeText element.
Combines sections under these conditions
- Sections under `combine_under_n_chars` characters are combined. The
default is 500.
### Testing
from unstructured.partition.html import partition_html
url = "https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-27-2023-0"
chunks = partition_html(url=url, chunking_strategy="by_title")
for chunk in chunks:
print(chunk)
print("\n\n" + "-"*80)
input()
If a layout model is used from unstructured-inference, you get back
class probabilities in the element metadata from partition.
extra-pdf-image-in in requirements already has the newest version of
unstructured-inference in there without a pinned version. Is there any
place else that the unstructured-inference version needs to be updated
to the required release version, 0.5.22?
Update `test_json` to not use auto partition due to dependencies. Previously, to run `test_json` requires full requirements installation library to read file types, including but not limited to, docx, pptx, as well as others. Therefore the test will raise error with base installation. With the update, this fix also add to other test files to check its invariant with `elements_to_json`.
Add test case test_partition_image_with_multipage_tiff that reads multipage TIFF file and
- confirms that the function reads all the pages in the TIFF.
- page number is added to the metadata
This PR is branched from and developed on top of 6d6be99 commit.
**Summary**
Closes#747
* Create CI Pipeline for running text, xml, email, and html doc tests
against the library installed without extras
* Create CI Pipeline for running each library extra against their
respective tests
