Minor bugfixes

• Small bugfix after the table recognition release

Table recognition model release!

• Add a new table recognition model that detects rows/columns and cells
• Add benchmarks for accuracy and speed (seems to be very accurate wrt to current state of the art open model)
• Improve memory efficiency of layout and text detection (hopefully no more memory leaks)
• Improve resolution handling for layout/text detection/ocr, which should improve accuracy quite a bit

OCR v2

A new version of the OCR model with a custom architecture.

• 20% faster
• Automatic language detection, with support for optional language hints
• Better accuracy on old/noisy documents
• Basic english handwriting support (to be improved soon)

Faster text detection + layout

Switched model architecture for the text detection and layout models:

• 30% faster on GPU
• 4x faster on CPU
• 12x faster on MPS (M series macs)

Accuracy should be about the same, or slightly better, from my benchmarks.

v0.4.14: Merge pull request #141 from VikParuchuri/dev

New transformers version added a new kwarg to donut embeddings. This now handles and ignores that kwarg, and also slightly future-proofs in case this happens again.

Minor bugfixes

• Fix rotation and copy bugs

Fix image bugs

• Fix bugs with RGBA images
• Fix assert bug
• Add back in thumbnail method for resizing
• Slightly optimize segformer code

Change image resize

• Image resize from cv2 to PIL - cv2 caused benchmark regressions

OCR speedups

• Speed up base OCR model ~15-20%, and reduce memory usage by ~25% (can do higher batch sizes)
• Add static cache for compilation - torch.compile will result in another 15% speedup
• Other optimizations, like faster image resizing
• Bugfixes, like enabling different length language inputs for OCR (batching different docs with different languages together)

Processor improvements

• Remove unneeded format conversions
• Fix bug in OCR, where only one color channel was used for OCR - results should be better now
• Speed up layout/text detection a bit