SoMark releases OXR, a 1.3 billion parameter open source document parsing model it says tops three benchmarks

Context

SoMark is the company behind OXR, a document parsing model. All of the benchmark claims are its own and have not been checked independently.

Deep dive

On October 20, SoMark introduced OXR with the line Beyond OCR, meaning beyond optical character recognition, the reading of text from images. It said the model has only 1.3 billion parameters and is open source.

SoMark claimed OXR ranks first on olmOCR Bench, OmniDocBench and DocParsingBench, and first in parsing speed among open source models. It said OXR is RSI powered, a first in document parsing, and turns documents into agent friendly data.

The video shows charts placing OXR at the top on accuracy against speed, with a note that speed was tested on an RTX PRO 6000 GPU. It also shows OXR converting 13 types of document elements, including chemical structures into SMILES strings, formulas into LaTeX and complex tables into HTML. A closing card says the model was post trained with recursive self improvement, which is what RSI refers to, and lists GitHub and Hugging Face links.

SoMark did not explain what recursive self improvement involved in training. The post asked users to star the project on GitHub, and it drew few likes relative to its views.

Document parsing models are widely used to feed PDFs and scans into AI systems, and smaller models are cheaper to run.

What to expect next

Watch for independent results on olmOCR Bench and OmniDocBench to confirm SoMark's rankings, and for details on what recursive self improvement meant in its training. Developers trying OXR on their own documents will show whether its speed and accuracy hold up. Also watch for responses from other open document parsing projects such as olmOCR and PaddleOCR. Its license terms on Hugging Face will also matter for commercial use.