Logics-Parsing Technical Report
Recent advances in Large Vision-Language models (LVLM) have spurred
significant progress in document parsing task. Compared to traditional
pipeline-based methods, end-to-end paradigms have shown their excellence in
converting PDF images into structured outputs through integrated Optical
Character Recognition (OCR), table recognition, mathematical formula
recognition and so on. However, the absence of explicit analytical stages for
document layouts and reading orders limits the LVLM's capability in handling
complex document types such as multi-column newspapers or posters. To address
this limitation, we propose in this report Logics-Parsing: an end-to-end
LVLM-based model augmented with reinforcement learning. Our model incorporates
meticulously designed reward mechanisms to optimize complex layout analysis and
reading order inference. In addition, we expand the model's versatility by
incorporating diverse data types such as chemical formulas and handwritten
Chinese characters into supervised fine-tuning. Finally, to enable rigorous
evaluation of our approach, we introduce LogicsParsingBench, a curated set of
1,078 page-level PDF images spanning nine major categories and over twenty
sub-categories, which will be released later. Comprehensive experiments
conducted on LogicsParsingBench have validated the efficacy and
State-of-the-art (SOTA) performance of our proposed model across diverse
document analysis scenarios. Project Page:
https://github.com/alibaba/Logics-Parsing
68d4a3f236950a9dff156891
Xiangyang Chen
68d4a3f236950a9dff156892
Shuzhao Li
68d4a3f236950a9dff156893
Xiuwen Zhu
admin_assigned
2025-09-25T11:56:06.220Z
6647a3e9f7e9d961cf4d375c
/avatars/5b6288207073f04dc3c8d1cf4e5613d2.svg
Xiuwen Zhu
user
xiuwenzhu
68d4a3f236950a9dff156894
Yongfan Chen
admin_assigned
2025-09-25T11:56:15.227Z
6576ba865f7efec0ae8ccadd
/avatars/35ce61f469e7d2b0b908baa8f00032d3.svg
Yongfan Chen
user
ZjuCv
68d4a3f236950a9dff156895
Fan Yang
68d4a3f236950a9dff156896
Cheng Fang
68d4a3f236950a9dff156897
Lin Qu
68d4a3f236950a9dff156898
Xiaoxiao Xu
68d4a3f236950a9dff156899
Hu Wei
68d4a3f236950a9dff15689a
Minggang Wu
Logics-Parsing Technical Report
Recent advances in Large Vision-Language models (LVLM) have spurred
significant progress in document parsing task. Compared to traditional
pipeline-based methods, end-to-end paradigms have shown their excellence in
converting PDF images into structured outputs through integrated Optical
Character Recognition (OCR), table recognition, mathematical formula
recognition and so on. However, the absence of explicit analytical stages for
document layouts and reading orders limits the LVLM's capability in handling
complex document types such as multi-column newspapers or posters. To address
this limitation, we propose in this report Logics-Parsing: an end-to-end
LVLM-based model augmented with reinforcement learning. Our model incorporates
meticulously designed reward mechanisms to optimize complex layout analysis and
reading order inference. In addition, we expand the model's versatility by
incorporating diverse data types such as chemical formulas and handwritten
Chinese characters into supervised fine-tuning. Finally, to enable rigorous
evaluation of our approach, we introduce LogicsParsingBench, a curated set of
1,078 page-level PDF images spanning nine major categories and over twenty
sub-categories, which will be released later. Comprehensive experiments
conducted on LogicsParsingBench have validated the efficacy and
State-of-the-art (SOTA) performance of our proposed model across diverse
document analysis scenarios. Project Page:
https://github.com/alibaba/Logics-Parsing
Logics-Parsing, an end-to-end LVLM model enhanced with reinforcement learning, improves document parsing by optimizing layout analysis and reading order inference, achieving state-of-the-art performance on a diverse benchmark.
Large Vision-Language models LVLM end-to-end paradigms Optical Character Recognition OCR table recognition mathematical formula recognition reinforcement learning reward mechanisms layout analysis reading order inference supervised fine-tuning chemical formulas handwritten Chinese characters LogicsParsingBench