- bump `unstructured-inference` to `0.6.6`
- specify default model name for element detection to be
`detectron2_onnx` to keep current behavior
- NOTE: the updated inference package by default would use yolox as
element detection model; this will be evaluated and enabled in a
separated PR
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
Closes GH Issue #1233.
### Summary
- add functionality to shrink all bounding boxes along x and y axes
(still centered around the same center point) before running xy-cut sort
### Evaluation
Run the followin gcommand for this
[PDF](https://utic-dev-tech-fixtures.s3.us-east-2.amazonaws.com/pastebin/patent-11723901-page2.pdf).
PYTHONPATH=. python examples/custom-layout-order/evaluate_xy_cut_sorting.py <file_path> <strategy>
Addresses
[#1332](https://github.com/Unstructured-IO/unstructured/issues/1332)
with `unstructured-inference` PR
[#208](https://github.com/Unstructured-IO/unstructured-inference/pull/208).
### Summary
- Add `image_path` to element metadata
- Pass parameters related to extracting images in PDF
- Preserve image elements ignored due to garbage text if
`el.metadata.image_path` is `True`
### Testing
from unstructured.partition.pdf import partition_pdf
f_path = "example-docs/embedded-images.pdf"
# default image output directory
elements = partition_pdf(
f_path,
strategy=strategy,
extract_images_in_pdf=True,
)
# specific image output directory
elements = partition_pdf(
f_path,
strategy=strategy,
extract_images_in_pdf=True,
image_output_dir_path=<directory path>,
)
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**
Adds logic to combine broken numbered list for pdf fast strategy.
**Details**
Previously the document reads the numbered list items part of the
`layout-parser-paper-fast.pdf` file as:
```
'1. An off-the-shelf toolkit for applying DL models for layout detection, character'
'recognition, and other DIA tasks (Section 3)'
'2. A rich repository of pre-trained neural network models (Model Zoo) that'
'underlies the off-the-shelf usage'
'3. Comprehensive tools for efficient document image data annotation and model'
'tuning to support different levels of customization'
'4. A DL model hub and community platform for the easy sharing, distribu- tion, and discussion of DIA models and pipelines, to promote reusability, reproducibility, and extensibility (Section 4)'
```
Now it reads:
```
'1. An off-the-shelf toolkit for applying DL models for layout detection, character recognition, and other DIA tasks (Section 3)'
'2. A rich repository of pre-trained neural network models (Model Zoo) that underlies the off-the-shelf usage'
'3. Comprehensive tools for efficient document image data annotation and model' tuning to support different levels of customization'
'4. A DL model hub and community platform for the easy sharing, distribu- tion, and discussion of DIA models and pipelines, to promote reusability, reproducibility, and extensibility (Section 4)'
```
The added logic leverages `ElementType` and `coordinates` to determine
whether the following lines is a part of the previously detected
`ListItem` or not.
**Test**
Add test that checks the element length less than original version with
broken numbered list. The test also checks whether the first detected
numbered list ends with previously broken line.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Klaijan <Klaijan@users.noreply.github.com>
Currently there are some cases when `partition_pdf` is run using the
`hi_res` strategy, in which elements can come back with category
`UncategorizedText`. This happens when the detection model fails to
detect an element, but we're able to find it anyway either because it
was embedded in the PDF, or we found it using OCR.
This commit is to allow for attempting to categorize these uncategorized
elements using our text-based classification function,
`element_from_text`.
Bumps unstructured-inference==05.23 to pull in @christinestraub's fix:
https://github.com/Unstructured-IO/unstructured-inference/pull/198 , so
embedded Images
in PDF's are now included in partition results ("hi_res").
From the perspective of elements with clean text, this is not a big win
as a lot of the images have OCR garbage. However, it is important to
preserve image elements for other downstream use cases, so overall this
is a step forward.
The issue was that for blocks detected in an image such as:
, where the full image is:
https://utic-dev-tech-fixtures.s3.us-east-2.amazonaws.com/pastebin//Users/cragwolfe/tmp/IRS-form-1987.png
, many ListItem's would be extracted that were not adding much value to
the output (assuming the block was determined to be of type List from
the layout model). This particular file is also used in ingest tests,
and you can see the prior output here:
https://github.com/Unstructured-IO/unstructured/blob/483b09b/test_unstructured_ingest/expected-structured-output/azure/IRS-form-1987.png.json#L93-L280
Test Instructions:
1. run the following snippet:
```
import json
import os
from datetime import datetime
from unstructured.__version__ import __version__
from unstructured.partition.auto import partition
from unstructured.staging.base import elements_to_json
filename = "/opt/home/tmp/IRS-form-1987.png"
output_dir = "/opt/home/tmp/json"
base_name_with_ext = os.path.basename(filename)
output_filename_part = os.path.join(output_dir, base_name_with_ext)
print(f"unstructured version: {__version__}")
#for strategy in ("hi_res", "fast", "auto"):
for strategy in ("hi_res",):
d1 = datetime.now()
elements = partition(filename=filename, strategy=strategy)
elems_as_dicts = json.loads(elements_to_json(elements, indent=2))
# strip out metadata for the sake of more readable results
for element_dict in elems_as_dicts:
del element_dict["metadata"]
json_filename=f"{output_filename_part}-{strategy}.json"
with open(json_filename, "w") as jsonf:
jsonf.write(json.dumps(elems_as_dicts, indent=2))
d2 = datetime.now()
print(f"num elements for {strategy}: {len(elements)}")
print(f"time elapsed {strategy}: {(d2-d1).total_seconds()}")
```
updating the `filename` and `output_dir` paths for your particular local
environment.
2. Open the json file that was writen to your `output_dir`, named
IRS-form-1987.png-hi_res.json
Witness the new element:
```
{
"type": "ListItem",
"element_id": "7d3ba328af2c20ddeef5d2c1d270f60f",
"text": "Long-term contracts.\u2014If you are required to change your method of accounting for long-term contracts under section 460, see Notice 87
-61 (9/21/87), 1987-38 IRB 40, for the notification procedures that must be followed Other methods. \u2014Unless the Service has Published a regulation
or procedure to the contrary, all other changes in accounting methods required by the Act are automatically considered to be approved by the Commissio
ner. Examples of method changes automatically approved by the Commissioner are those changes required to effect: (1) the repeal of the reserve method f
or bad debts of taxpayers other than financial institutions (Act section 805); (2) the repeal of the installment method for sales under a revolving cre
dit plan (Act section 812); (3) the Inclusion of income attributable to the sale or furnishing of utility services no later than the year in which the
services were provided to customers (Act section 821); and (4) the repeal of the deduction for qualified discount coupons (Act section 823). Do not fil
e Form 3115 for these changes."
},
```
### Summary
Address
[#1136](https://github.com/Unstructured-IO/unstructured/issues/1136) for
`hi_res` and `fast` strategies. The `ocr_only` strategy does not include
coordinates.
- add functionality to switch sort mode between the current `basic`
sorting and the new `xy-cut` sorting for `hi_res` and `fast` strategies
- add the script to evaluate the `xy-cut` sorting approach
- add jupyter notebook to provide evaluation and visualization for the
`xy-cut` sorting approach
### Evaluation
```
export PYTHONPATH=.:$PYTHONPATH && python examples/custom-layout-order/evaluate_xy_cut_sorting.py <file_path> <strategy>
```
Here, the file should be under the project root directory. For example,
```
export PYTHONPATH=.:$PYTHONPATH && python examples/custom-layout-order/evaluate_xy_cut_sorting.py example-docs/multi-column-2p.pdf fast
```
* pip-compile in order to bump unstructured-inference
* Set the default `ocr_mode` back to `enitre_page` now that [this
error](https://github.com/Unstructured-IO/unstructured-inference/pull/183)
is addressed
* Explicitly add `sphinx-tabs` to `build.in`. This file provides
`docs/requirements.txt`.
* Remove a pinned `pydantic` version
* Fix a makefile command to `pip-compile` a missing ingest file.
Set to individual_blocks for now to work around [this
bug](https://github.com/Unstructured-IO/unstructured-inference/issues/179).
I verified by printing the current ocr_mode in inference. The
`entire_page` default is overridden.
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: awalker4 <awalker4@users.noreply.github.com>
Bump to unstructured-inference==0.5.13, which includes:
Fix extracted image elements being included in layout merge, addresses the issue
where an entire-page image in a PDF was not passed to the layout model when using hi_res.
* add param
* expected test
* add option (to do doc nit)
* test with api for now
* typo
* test with api key
* use local only
* encoding -> partition-encoding
* changelog and version
* Update ingest test fixtures (#1055)
Co-authored-by: yuming-long <yuming-long@users.noreply.github.com>
* ignore coordinates
* no witespace lol
* Update ingest test fixtures (#1061)
Co-authored-by: yuming-long <yuming-long@users.noreply.github.com>
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: yuming-long <yuming-long@users.noreply.github.com>
