chore(deps): update dependency unstructured to v0.15.13

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
unstructured	== 0.15.9 -> ==0.15.13

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Release Notes

Unstructured-IO/unstructured (unstructured)

v0.15.13

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BREAKING CHANGES

• Remove dead experimental code. Unused code in file_utils.experimental and file_utils.metadata was removed. These functions were never published in the documentation, but if a client dug these out and used them this removal could break client code.

Enhancements

• Improve pdfminer image cleanup process. Optimized the removal of duplicated pdfminer images by performing the cleanup before merging elements, rather than after. This improvement reduces execution time and enhances overall processing speed of PDF documents.

Features

Fixes

• Fixes high memory overhead for intersection area computation Using numpy.float32 for coordinates and remove intermediate variables to reduce memory usage when computing intersection areas
• Fixes the arm64 image build arm64 builds are now fixed and will be available against starting with the 0.15.13 release.

v0.15.12

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Enhancements

• Improve pdfminer element processing Implemented splitting of pdfminer elements (groups of text chunks) into smaller bounding boxes (text lines). This prevents loss of information from the object detection model and facilitates more effective removal of duplicated pdfminer text.

Features

Fixes

• Fixed table accuracy metric Table accuracy was incorrectly using column content difference in calculating row accuracy.

v0.15.10

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Enhancements

• Enhance pdfminer element cleanup Expand removal of pdfminer elements to include those inside all non-pdfminer elements, not just tables.
• Modified analysis drawing tools to dump to files and draw from dumps If the parameter analysis of the partition_pdf function is set to True, the layout for Object Detection, Pdfminer Extraction, OCR and final layouts will be dumped as json files. The drawers now accept dict (dump) objects instead of internal classes instances.
• Vectorize pdfminer elements deduplication computation. Use numpy operations to compute IOU and sub-region membership instead of using simply loop. This improves the speed of deduplicating elements for pages with a lot of elements.

Features

Fixes

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