POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models
  for Document Conversion
High-quality labeled data is essential for training accurate document
conversion models, particularly in domains with complex formats such as tables,
formulas, and multi-column text. However, manual annotation is both costly and
time-consuming, while automatic labeling using existing models often lacks
accuracy in handling such challenging scenarios. Consequently, training student
models by distilling outputs from teacher models can significantly limit their
performance in real-world applications. In this paper, we propose a fully
automated, distillation-free framework comprising two stages for constructing
high-quality document extraction datasets and models capable of handling
diverse document formats and layouts. In the first stage, we introduce a method
for generating large-scale, diverse synthetic data, which enables a model to
extract key elements in a unified format with strong initial performance. In
the second stage, we present a self-improvement approach that further adapts
the model, initially trained on synthetic data, to real-world documents.
Specifically, we first use the fine-tuned model to annotate real documents,
then apply a suite of filtering strategies to verify annotation quality, and
finally retrain the model on the verified dataset. By iteratively repeating
this process, we progressively enhance both the model's conversion capabilities
and the quality of the generated data. We train a public POINTS-1.5 model to
obtain POINTS-Reader, which surpasses many existing public and proprietary
models of comparable or larger size. Our model is available at
https://github.com/Tencent/POINTS-Reader.
68b7bc71295f15ff60911355
Yuan Liu
claimed_verified
2025-09-03T09:31:38.449Z
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Yuan Liu
user
YuanLiuuuuuu
68b7bc71295f15ff60911356
Zhongyin Zhao
68b7bc71295f15ff60911357
Le Tian
68b7bc71295f15ff60911358
Haicheng Wang
68b7bc71295f15ff60911359
Xubing Ye
claimed_verified
2025-09-03T08:26:46.461Z
6584179da76333af9f61e90b
https://cdn-avatars.huggingface.co/v1/production/uploads/6584179da76333af9f61e90b/J63cWvnALEddg42h1p3Ew.jpeg
Xubing Ye
user
YxxxB
68b7bc71295f15ff6091135a
Yangxiu You
68b7bc71295f15ff6091135b
Zilin Yu
68b7bc71295f15ff6091135c
Chuhan Wu
68b7bc71295f15ff6091135d
Xiao Zhou
68b7bc71295f15ff6091135e
Yang Yu
68b7bc71295f15ff6091135f
Jie Zhou
POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models
  for Document Conversion
High-quality labeled data is essential for training accurate document
conversion models, particularly in domains with complex formats such as tables,
formulas, and multi-column text. However, manual annotation is both costly and
time-consuming, while automatic labeling using existing models often lacks
accuracy in handling such challenging scenarios. Consequently, training student
models by distilling outputs from teacher models can significantly limit their
performance in real-world applications. In this paper, we propose a fully
automated, distillation-free framework comprising two stages for constructing
high-quality document extraction datasets and models capable of handling
diverse document formats and layouts. In the first stage, we introduce a method
for generating large-scale, diverse synthetic data, which enables a model to
extract key elements in a unified format with strong initial performance. In
the second stage, we present a self-improvement approach that further adapts
the model, initially trained on synthetic data, to real-world documents.
Specifically, we first use the fine-tuned model to annotate real documents,
then apply a suite of filtering strategies to verify annotation quality, and
finally retrain the model on the verified dataset. By iteratively repeating
this process, we progressively enhance both the model's conversion capabilities
and the quality of the generated data. We train a public POINTS-1.5 model to
obtain POINTS-Reader, which surpasses many existing public and proprietary
models of comparable or larger size. Our model is available at
https://github.com/Tencent/POINTS-Reader.
A framework for constructing high-quality document extraction datasets and models through synthetic data generation and iterative self-improvement outperforms existing models.
document conversion models synthetic data self-improvement annotation quality POINTS-1.5 POINTS-Reader