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docs/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL-1.5.en.md
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## 1. PaddleOCR-VL-1.5 Introduction
**PaddleOCR-VL-1.5** is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency.
### Key Metrics:
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/paddleocr-vl-1.5_metrics.png" width="800"/>
</div>
### **Core Features**
1. With a **parameter size of 0.9B**, PaddleOCR-VL-1.5 **achieves xxx% accuracy on OmniDocBench v1.5**, surpassing the previous SOTA model PaddleOCR-VL. Significant improvements are observed in **table, formula, and text understanding.**
2. **It introduces an innovative approach to document parsing by supporting irregular-shaped localization**, enabling accurate polygonal detection under skewed and curved document conditions. Evaluations across five real-world scenarios—scanning, curving, skewing, screen-photo capture, and light variation—demonstrate superior performance over mainstream open-source and proprietary models.
3. The model introduces **text spotting (text-line localization and recognition)**, along with **seal recognition**, with all corresponding metrics **setting new SOTA results** in their respective tasks.
4. PaddleOCR-VL-1.5 further strengthens its capability in **specialized scenarios and multilingual recognition.** Recognition performance is improved for **rare characters, ancient texts, multilingual tables, underlines, and checkboxes,** and language coverage is extended to include **China's Tibetan script and Bengali.**
5. The model supports **automatic cross-page table merging** and **cross-page paragraph heading recognition**, effectively mitigating content fragmentation issues in **long-document parsing.**
## 2. Model Architecture
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/PaddleOCR-VL-1.5.png" width="800"/>
</div>
## 3. Model Performance
### 1. OmniDocBench v1.5
#### PaddleOCR-VL achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/omnidocbenchv1.5_metrics.png" width="800"/>
</div>
> **Notes:**
> - Performance metrics are cited from the [OmniDocBench official leaderboard](https://opendatalab.com/omnidocbench), except for Gemini-3 Pro, Qwen3-VL-235B-A22B-Instruct and our model, which were evaluated independently.
### 2. Real5-OmniDocBench
#### Across all five diverse and challenging scenarios—scanning, warping, screen-photography, illumination, and skew—PaddleOCR-VL-1.5 consistently sets new SOTA records
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/real5-omnidocbench_metrics.png" width="800"/>
</div>
> **Notes:**
> - Real5-OmniDocBench is a brand-new benchmark oriented toward real-world scenarios, which we constructed based on the OmniDocBench v1.5 dataset. The dataset comprises five distinct scenarios: Scanning, Warping, Screen-photography, Illumination, and Skew. For further details, please refer to [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
## 4、Inference and deployment Performance {#4-inference-and-deployment-performance}
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/inference_performance.png" width="600"/>
</div>
> **Notes:**
> - End-to-End Inference Performance Comparison on OmniDocBench v1.5. PDF documents were processed in batches of 512 on a single NVIDIA A100 GPU. The reported end-to-end runtime includes both PDF rendering and Markdown generation. All methods rely on their built-in PDF parsing modules and default DPI settings to reflect out-of-the-box performance.
## 5. Visualization
### Real-word Document Parsing
#### Illumination
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/light.jpg" width="800"/>
</div>
#### Skew
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/skew.jpg" width="800"/>
</div>
#### Screen Photography
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/screen.jpg" width="800"/>
</div>
#### Scanning
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/scaning.jpg" width="800"/>
</div>
#### Warping
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/curving.jpg" width="800"/>
</div>
### Text Spotting
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/spotting.jpg" width="800"/>
</div>
### Seal Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/seal.jpg" width="800"/>
</div>
docs/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL-1.5.md
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## 1. PaddleOCR-VL-1.5 简介
**PaddleOCR-VL-1.5** 在1.0版本上进行了进一步能力的扩展和升级优化，在文档解析 OmniDocBench v1.5 上取得了 94.5% 的更高的新 SOTA（最佳）结果。为了严格评估其对现实世界物理畸变的鲁棒性——包括扫描伪影、倾斜、弯曲、屏摄和光照变化——我们提出了 Real5-OmniDocBench 基准测试。实验结果表明，该增强模型在这一新构建的基准测试中各个场景都达到了 SOTA 性能。此外，我们通过加入印章识别和文字检测识别任务扩展了模型能力，同时保持了 0.9B 的超紧凑 VLM 规模和高效率。
### **关键指标:**
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/paddleocr-vl-1.5_metrics.png" width="800"/>
</div>
### **核心特性:**
1. **文档解析的SOTA性能：** 凭借 0.9B 的参数量，PaddleOCR-VL-1.5 在 OmniDocBench v1.5 上达到了 94.5% 的准确率，超越了之前的 SOTA 模型 PaddleOCR-VL。在表格、公式和文本识别方面观察到了显著提升。
2. **现实5大场景文档解析的SOTA性能：** 引入了一种创新的文档解析方法，支持不规则形状定位，能够在文档倾斜和弯曲条件下实现精确的多边形检测。在扫描、弯曲、倾斜、屏摄和光照变化这五个现实场景的评估中，表现优于主流的开源和闭源模型。
3. **0.9B紧凑架构扩充能力：** 模型引入了文本行定位与识别 以及 印章识别，所有相关指标均在各自任务中创下了新的 SOTA 结果。
4. **强化多元素识别能力：** PaddleOCR-VL-1.5 进一步增强了在特定场景和多语言识别方面的能力。针对特殊符号、古籍、多语言表格、下划线和复选框的识别性能得到提升，语言覆盖范围扩展至包括中国藏文和孟加拉语。
5. **长文档跨页解析：** 模型支持跨页表格自动合并和跨页段落标题识别，有效缓解了长文档解析中的内容碎片化问题。
## 二、技术架构
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/PaddleOCR-VL-1.5.png" width="800"/>
</div>
## 三、 模型性能
### 1. OmniDocBench v1.5
#### PaddleOCR-VL 在 OmniDocBench v1.5 上的整体、文本、公式、表格和阅读顺序中均达到最先进的性能。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/omnidocbenchv1.5_metrics.png" width="800"/>
</div>
> **注:**
> - 性能指标引自 [OmniDocBench 官方排行榜](https://opendatalab.com/omnidocbench), Gemini-3 Pro、Qwen3-VL-235B-A22B-Instruct 和我们的模型除外。
### 2. Real5-OmniDocBench
#### 在扫描、扭曲、屏摄、光照和倾斜这五个多样化且具挑战性的场景中，PaddleOCR-VL-1.5 均创下了新的 SOTA 记录。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/real5-omnidocbench_metrics.png" width="800"/>
</div>
> **注:**
> - Real5-OmniDocBench 是我们基于 OmniDocBench v1.5 数据集构建的、面向真实场景的全新基准测试。该数据集包含五个不同场景：扫描 (Scanning)、扭曲 (Warping)、屏摄 (Screen-photography)、光照 (Illumination) 和倾斜 (Skew)。更多详情请参阅 [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
## 4、推理部署性能 {#4推理部署性能}
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/inference_performance.png" width="600"/>
</div>
> **注:**
> - OmniDocBench v1.5 上的端到端推理性能对比。PDF 文档在单张 NVIDIA A100 GPU 上以 512 的 batch size 进行处理。报告的端到端运行时间包含 PDF 渲染和 Markdown 生成。所有方法均依赖其内置的 PDF 解析模块和默认 DPI 设置，以反映开箱即用的性能。
## 5. 可视化
### 现实场景文档
#### 光照
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/light.jpg" width="800"/>
</div>
#### 倾斜
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/skew.jpg" width="800"/>
</div>
#### 屏摄
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/screen.jpg" width="800"/>
</div>
#### 扫描
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/scaning.jpg" width="800"/>
</div>
#### 弯曲/扭曲
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/curving.jpg" width="800"/>
</div>
### 文本定位与识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/spotting.jpg" width="800"/>
</div>
### 印章识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/seal.jpg" width="800"/>
</div>
docs/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL-1.6.en.md
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## 1. Introduction to PaddleOCR-VL-1.6
**PaddleOCR-VL-1.6** further optimizes PaddleOCR-VL-1.5 by systematically analyzing under-optimized areas in the current model, applying targeted data optimization, and adopting refined post-training strategies. It achieves a new state-of-the-art (SOTA) result of 96.33% on the OmniDocBench v1.6 document parsing benchmark. PaddleOCR-VL-1.6 also reaches SOTA performance across all scenarios on Real5-OmniDocBench, a benchmark designed to evaluate robustness against real-world physical distortions. In addition, PaddleOCR-VL-1.6 outperforms PaddleOCR-VL-1.5 on three subtasks: seal recognition, text detection and recognition, and chart recognition, while still maintaining an ultra-compact 0.9B-parameter VLM and high efficiency.
### **Key Metrics:**
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/paddleocr-vl-1.6_metrics.png" width="800"/>
</div>
### **Core Features:**
1. **SOTA performance in document parsing:** With only 0.9B parameters, PaddleOCR-VL-1.6 achieves 96.33% accuracy on OmniDocBench v1.6, surpassing the previous SOTA model, PaddleOCR-VL-1.5. Significant improvements are observed in table, formula, and text recognition.
2. **SOTA performance for document parsing across five real-world scenarios:** PaddleOCR-VL-1.6 offers stronger robustness and practicality in real-world use cases. In evaluations across five real-world distortion scenarios—scanning, warping, skew, screen photography, and illumination variation—it outperforms mainstream open-source and closed-source models.
3. **Enhanced multi-element recognition capabilities:** Beyond improved layout parsing, PaddleOCR-VL-1.6 substantially strengthens recognition of complex tables, ancient books, and rare Chinese characters, while further improving three existing capabilities: chart parsing, seal recognition, and text detection and recognition.
4. **Compact 0.9B architecture:** PaddleOCR-VL-1.6 follows the compact 0.9B architecture of the PaddleOCR-VL series, enabling zero-cost adaptation and drop-in replacement.
## 2. Technical Architecture
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/overall.png" width="800"/>
</div>
1. **Data engine:** Starting from PaddleOCR-VL-1.5, the data engine systematically identifies under-optimized areas in PaddleOCR-VL-1.5, designs strategies for obtaining high-quality labels, and performs targeted data optimization.
2. **Progressive post-training strategy:** Data is carefully categorized from three perspectives: quality, difficulty, and improvement value. The training weights of PaddleOCR-VL-1.5 are loaded, and a three-stage post-training strategy—continued pre-training, supervised fine-tuning, and reinforcement learning—is applied according to different data quality levels to steadily improve model performance.
## 3. Model Performance
### 1. OmniDocBench v1.6
#### PaddleOCR-VL-1.6 achieves state-of-the-art performance on OmniDocBench v1.6 in overall metrics, text, formulas, and tables. It also delivers leading results in reading order.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/table2.png" width="800"/>
</div>
> **Note:**
> - Performance metrics are cited from the [official OmniDocBench leaderboard](https://opendatalab.com/omnidocbench).
### 2. Real5-OmniDocBench
#### PaddleOCR-VL-1.6 sets new SOTA records across five diverse and challenging scenarios: scanning, warping, screen photography, illumination, and skew.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/table3.png" width="800"/>
</div>
> **Note:**
> - Real5-OmniDocBench is a new real-world benchmark built by the PaddleOCR team based on the OmniDocBench v1.5 dataset. It contains five scenarios: Scanning, Warping, Screen-photography, Illumination, and Skew. For more details, see [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
## 4. Inference and Deployment Performance
PaddleOCR-VL-1.6 and PaddleOCR-VL-1.5 use exactly the same model architecture design, so they have identical inference speeds. For details about the inference speed of PaddleOCR-VL-1.5, refer to [PaddleOCR-VL-1.5 inference speed](./PaddleOCR-VL-1.5.md#4-inference-and-deployment-performance).
## 5. Visualization
### Comparison with PaddleOCR-VL-1.5
#### Ancient Book Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/ancient1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/ancient2.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/ancient3.png" width="800"/>
</div>
#### Chart Parsing
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/chart1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/chart2.png" width="800"/>
</div>
#### Formula Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/fomula1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/fomula2.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/fomula3.png" width="800"/>
</div>
#### Rare Chinese Character Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/rare-cha1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/rare-cha2.png" width="800"/>
</div>
#### Seal Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/seal1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/seal2.png" width="800"/>
</div>
### Table Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/table1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/table2.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/table3.png" width="800"/>
</div>
docs/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL-1.6.md
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## 1. PaddleOCR-VL-1.6 简介
**PaddleOCR-VL-1.6** 在 PaddleOCR-VL-1.5 的基础上进一步优化，通过系统分析当前模型中的欠优化区域，进行针对性数据优化，并采用精细化的后训练策略，在文档解析基准 OmniDocBench v1.6 上取得 96.33% 的最新 SOTA（最佳）结果。同时，PaddleOCR-VL-1.6 在面向真实世界物理畸变鲁棒性的 Real5-OmniDocBench 基准测试中，也在各个场景下均达到 SOTA 性能。此外，在印章识别、文字检测识别和图表识别三个子任务上，PaddleOCR-VL-1.6 均领先 PaddleOCR-VL-1.5，同时依然保持 0.9B 超紧凑 VLM 参数量和高效率。
### **关键指标:**
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/paddleocr-vl-1.6_metrics.png" width="800"/>
</div>
### **核心特性:**
1. **文档解析的SOTA性能：** 凭借 0.9B 的参数量，PaddleOCR-VL-1.6 在 OmniDocBench v1.6 上达到了 96.33% 的准确率，超越了之前的 SOTA 模型 PaddleOCR-VL-1.5。在表格、公式和文本识别方面观察到了显著提升。
2. **现实5大场景文档解析的SOTA性能：** 具备更强的鲁棒性和真实场景的实用性。在扫描、弯曲、倾斜、屏摄和光照变化这五个真实扰动场景的评估中，表现优于主流的开源和闭源模型。
3. **强化多元素识别能力：** 除了版面解析能力的提升外，PaddleOCR-VL-1.6 进一步大幅度增强了对复杂表格，古籍 和 生僻字的识别能力，同时在 图表解析，印章识别，文字检测识别这三个原有能力上进一步提升。
4. **0.9B紧凑架构：** 沿用 PaddleOCR-VL系列的 0.9B 紧凑构架，零成本适配，即换即用。
## 二、技术架构
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/overall.png" width="800"/>
</div>
1. **数据引擎:** 以 PaddleOCR-VL-1.5 为出发点，系统性定位 PaddleOCR-VL-1.5 的欠优化区域，并设计高质量标签获取策略，针对性的进行数据优化。
2. **渐进式后训练策略** 从质量，难度，提升价值三个角度精细化划分数据，加载 PaddleOCR-VL-1.5 训练权重，结合不同的数据质量进行 继续预训练，监督微调，强化学习 三阶段的后训练策略，稳步提升模型性能。
## 三、 模型性能
### 1. OmniDocBench v1.6
#### PaddleOCR-VL-1.6 在 OmniDocBench v1.6 上的整体指标、文本、公式、表格均达到最先进的性能，其中在在阅读顺序方面，PaddleOCR-VL-1.6也取得了较为领先的指标。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/table2.png" width="800"/>
</div>
> **注:**
> - 性能指标引自 [OmniDocBench 官方排行榜](https://opendatalab.com/omnidocbench)。
### 2. Real5-OmniDocBench
#### 在扫描、扭曲、屏摄、光照和倾斜这五个多样化且具挑战性的场景中，PaddleOCR-VL-1.6 均创下了新的 SOTA 记录。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/table3.png" width="800"/>
</div>
> **注:**
> - Real5-OmniDocBench 是 PaddleOCR团队 基于 OmniDocBench v1.5 数据集构建的、面向真实场景的全新基准测试。该数据集包含五个不同场景：扫描 (Scanning)、扭曲 (Warping)、屏摄 (Screen-photography)、光照 (Illumination) 和倾斜 (Skew)。更多详情请参阅 [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
## 四、推理部署性能
PaddleOCR-VL-1.6 和 PaddleOCR-VL-1.5 采用完全相同的模型架构设计，因此有完全相同的推理速度。关于PaddleOCR-VL-1.5推理速度的说明可以参考 [PaddleOCR-VL-1.5推理速度](./PaddleOCR-VL-1.5.md#4推理部署性能) 。
## 5. 可视化
### 和 PaddleOCR-VL-1.5 的对比
#### 古籍识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/ancient1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/ancient2.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/ancient3.png" width="800"/>
</div>
#### 图表解析
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/chart1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/chart2.png" width="800"/>
</div>
#### 公式识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/fomula1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/fomula2.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/fomula3.png" width="800"/>
</div>
#### 生僻字识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/rare-cha1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/rare-cha2.png" width="800"/>
</div>
#### 印章识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/seal1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/seal2.png" width="800"/>
</div>
### 表格识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/table1.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/table2.png" width="800"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_6/comparison/table3.png" width="800"/>
</div>
docs/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.en.md
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## 1. PaddleOCR-VL Introduction
**PaddleOCR-VL** is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios.
### Key Metrics:
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/allmetric.png" width="800"/>
</div>
### **Core Features**
1. **Compact yet Powerful VLM Architecture:** We present a novel vision-language model that is specifically designed for resource-efficient inference, achieving outstanding performance in element recognition. By integrating a NaViT-style dynamic high-resolution visual encoder with the lightweight ERNIE-4.5-0.3B language model, we significantly enhance the model’s recognition capabilities and decoding efficiency. This integration maintains high accuracy while reducing computational demands, making it well-suited for efficient and practical document processing applications.
2. **SOTA Performance on Document Parsing:** PaddleOCR-VL achieves state-of-the-art performance in both page-level document parsing and element-level recognition. It significantly outperforms existing pipeline-based solutions and exhibiting strong competitiveness against leading vision-language models (VLMs) in document parsing. Moreover, it excels in recognizing complex document elements, such as text, tables, formulas, and charts, making it suitable for a wide range of challenging content types, including handwritten text and historical documents. This makes it highly versatile and suitable for a wide range of document types and scenarios.
3. **Multilingual Support:** PaddleOCR-VL Supports 109 languages, covering major global languages, including but not limited to Chinese, English, Japanese, Latin, and Korean, as well as languages with different scripts and structures, such as Russian (Cyrillic script), Arabic, Hindi (Devanagari script), and Thai. This broad language coverage substantially enhances the applicability of our system to multilingual and globalized document processing scenarios.
## 2. Model Architecture
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/methods/paddleocrvl.png" width="800"/>
</div>
## 3. Model Performance
### Page-Level Document Parsing
#### OmniDocBench v1.5
##### PaddleOCR-VL achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/omni15.png" width="800"/>
</div>
#### OmniDocBench v1.0
##### PaddleOCR-VL achieves SOTA performance for almost all metrics of overall, text, formula, tables and reading order on OmniDocBench v1.0.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/omni10.png" width="800"/>
</div>
### Element-level Recognition
#### Text
**Comparison of OmniDocBench-OCR-block Performance**
PaddleOCR-VL’s robust and versatile capability in handling diverse document types, establishing it as the leading method in the OmniDocBench-OCR-block performance evaluation.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/omnibenchocr.png" width="800"/>
</div>
**Comparison of In-house-OCR-block Performance**
In-house-OCR provides a evaluation of performance across multiple languages and text types. Our model demonstrates outstanding accuracy with the lowest edit distances in all evaluated scripts.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhouseocr.png" width="800"/>
</div>
#### Table
**Comparison of In-house-Table Performance**
Our self-built evaluation set contains diverse types of table images, such as Chinese, English, mixed Chinese-English, and tables with various characteristics like full, partial, or no borders, book/manual formats, lists, academic papers, merged cells, as well as low-quality, watermarked, etc. PaddleOCR-VL achieves remarkable performance across all categories.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhousetable.png" width="600"/>
</div>
#### Formula
**Comparison of In-house-Formula Performance**
In-house-Formula evaluation set contains simple prints, complex prints, camera scans, and handwritten formulas. PaddleOCR-VL demonstrates the best performance in every category.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhouse-formula.png" width="500"/>
</div>
#### Chart
**Comparison of In-house-Chart Performance**
The evaluation set is broadly categorized into 11 chart categories, including bar-line hybrid, pie, 100% stacked bar, area, bar, bubble, histogram, line, scatterplot, stacked area, and stacked bar. PaddleOCR-VL not only outperforms expert OCR VLMs but also surpasses some 72B-level multimodal language models.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhousechart.png" width="400"/>
</div>
## 4、Inference and deployment Performance
To improve the inference performance of PaddleOCR-VL, we introduce multi-threading asynchronous execution into the inference workflow. The process is divided into three main stages—data loading (e.g., rendering PDF pages as images), layout model processing, and VLM inference—each running in a separate thread. Data is transferred between adjacent stages via queues, enabling concurrent execution for higher efficiency. We measured the end-to-end inference speed and GPU usage on the OmniDocBench v1.0 dataset, processing PDF files in batches of 512 on a single NVIDIA A100 GPU.
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inference.png" width="600"/>
</div>
> **Notes:** <sup>&#8224;</sup> means vLLM backend，<sup>&#8225;</sup> means sglang backend.
## 5. Visualization
### Comprehensive Document Parsing
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview1.jpg" width="600"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview2.jpg" width="600"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview3.jpg" width="600"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview4.jpg" width="600"/>
</div>
### Text Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/text_english_arabic.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/text_handwriting_02.jpg" width="300"/>
</div>
### Table Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/table_01.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/table_02.jpg" width="300"/>
</div>
### Formula Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/formula_EN.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/formula_EN.jpg" width="300"/>
</div>
### Chart Recognition
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/chart_01.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/chart_02.jpg" width="300"/>
</div>
## 6. FAQ
**1.How to use PaddleOCR-VL for document parsing?**
Please refer to our usage documentation [PaddleOCR-VL Usage](../../pipeline_usage/PaddleOCR-VL.en.md).
**2. How to fine-tune the PaddleOCR-VL model?**
Currently, we do not support fine-tuning of the model, but it is a high-priority feature and will be released soon. Please stay tuned.
**3. Why was my chart not recognized and how can I use chart recognition?**
Because our default chart recognition function is turned off, it needs to be manually turned on. Please refer to [PaddleOCR-VL Usage](../../pipeline_usage/PaddleOCR-VL.en.md) and set the use_chart_recognition为True parameters to True turn it on.
**4. What are the 109 supported languages?**
Chinese, English, Korea, Japanese, Thai, Greek, Tamil, Telugu
Arabic: Arabic, Persian, Uyghur, Urdu, Pashto, Kurdish, Sindhi, Balochi
Latin: French, German, Afrikaans, Italian, Spanish, Bosnian, Portuguese, Czech, Welsh, Danish, Estonian, Irish, Croatian, Uzbek, Hungarian, Serbian (Latin), Indonesian, Occitan, Icelandic, Lithuanian, Maori, Malay, Dutch, Norwegian, Polish, Slovak, Slovenian, Albanian, Swedish, Swahili, Tagalog, Turkish, Latin, Azerbaijani, Kurdish, Latvian, Maltese, Pali, Romanian, Vietnamese, Finnish, Basque, Galician, Luxembourgish, Romansh, Catalan, Quechua
Cyrillic: Russian, Belarusian, Ukrainian, Serbian (Cyrillic), Bulgarian, Mongolian, Abkhazian, Adyghe, Kabardian, Avar, Dargin, Ingush, Chechen, Lak, Lezgin, Tabasaran, Kazakh, Kyrgyz, Tajik, Macedonian, Tatar, Chuvash, Bashkir, Malian, Moldovan, Udmurt, Komi, Ossetian, Buryat, Kalmyk, Tuvan, Sakha, Karakalpak
Devanagari: Hindi, Marathi, Nepali, Bihari, Maithili, Angika, Bhojpuri, Magahi, Santali, Newari, Konkani, Sanskrit, Haryanvi
**5. If the results of the layout check are not satisfactory, what solutions can be optimized?**
Since layout detection is mainly trained for various document scenarios, if your test data is non-standard documents such as license plates, tickets images or ID Cards and you want to do OCR recognition, you can directly use the PaddleOCR-VL-0.9B model and turn off the layout detection model by setting use_layout_detection to False. If you find any layout detection errors, you can directly try the effect of using PaddleOCR-VL-0.9B alone.
We recommend using the [ERNIEKit toolkit](https://github.com/PaddlePaddle/ERNIE/tree/release/v1.4) to perform Supervised Fine-Tuning (SFT) on the PaddleOCR-VL-0.9B model. For detailed steps, please refer to the [ERNIEKit documentation](https://github.com/PaddlePaddle/ERNIE/blob/release/v1.4/docs/paddleocr_vl_sft.md).
docs/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.md
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## 一、PaddleOCR-VL简介
**PaddleOCR-VL** 是一款先进、高效的文档解析模型，专为文档中的元素识别设计。其核心组件为 PaddleOCR-VL-0.9B，这是一种紧凑而强大的视觉语言模型（VLM），它由 NaViT 风格的动态分辨率视觉编码器与 ERNIE-4.5-0.3B 语言模型组成，能够实现精准的元素识别。该模型支持 109 种语言，并在识别复杂元素（如文本、表格、公式和图表）方面表现出色，同时保持极低的资源消耗。通过在广泛使用的公开基准与内部基准上的全面评测，PaddleOCR-VL 在页级级文档解析与元素级识别均达到 SOTA 表现。它显著优于现有的基于Pipeline方案和文档解析多模态方案以及先进的通用多模态大模型，并具备更快的推理速度。这些优势使其非常适合在真实场景中落地部署。
### **关键指标:**
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/allmetric.png" width="800"/>
</div>
### **核心特性:**
1. **紧凑而强大的视觉语言模型架构：** 我们提出了一种新的视觉语言模型，专为资源高效的推理而设计，在元素识别方面表现出色。通过将NaViT风格的动态高分辨率视觉编码器与轻量级的ERNIE-4.5-0.3B语言模型结合，我们显著增强了模型的识别能力和解码效率。这种集成在保持高准确率的同时降低了计算需求，使其非常适合高效且实用的文档处理应用。
2. **文档解析的SOTA性能：** PaddleOCR-VL在页面级文档解析和元素级识别中达到了最先进的性能。它显著优于现有的基于流水线的解决方案，并在文档解析中展现出与领先的视觉语言模型（VLMs）竞争的强劲实力。此外，它在识别复杂的文档元素（如文本、表格、公式和图表）方面表现出色，使其适用于包括手写文本和历史文献在内的各种具有挑战性的内容类型。这使得它具有高度的多功能性，适用于广泛的文档类型和场景。
3. **多语言支持：** PaddleOCR-VL支持109种语言，覆盖了主要的全球语言，包括但不限于中文、英文、日文、拉丁文和韩文，以及使用不同文字和结构的语言，如俄语（西里尔字母）、阿拉伯语、印地语（天城文）和泰语。这种广泛的语言覆盖大大增强了我们系统在多语言和全球化文档处理场景中的适用性。
## 二、技术架构
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/methods/paddleocrvl.png" width="800"/>
</div>
## 三、 模型性能
### 页面级文档解析
#### 1. OmniDocBench v1.5
##### PaddleOCR-VL 在 OmniDocBench v1.5 上的整体、文本、公式、表格和阅读顺序中均达到最先进的性能。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/omni15.png" width="800"/>
</div>
#### 2. OmniDocBench v1.0
##### PaddleOCR-VL 在 OmniDocBench v1.0 的整体、文本、公式、表格以及阅读顺序等几乎所有评估指标上均达到了 SOTA 性能。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/omni10.png" width="800"/>
</div>
### 元素级识别
#### 文本
**OmniDocBench-OCR-block**
PaddleOCR-VL 在处理多样化文档类型方面展现出强大而灵活的能力，使其在 OmniDocBench-OCR-block 的性能评估中成为领先方法。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/omnibenchocr.png" width="800"/>
</div>
**In-house-OCR-block**
我们自建的评测集评估了模型在多语言和多文本类型下的性能。我们的模型在所有评测文字体系中均表现出卓越的准确性，并取得了最低的编辑距离。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhouseocr.png" width="800"/>
</div>
#### 表格
**In-house-Table**
我们自建的评测集包含多种类型的表格图像，例如中文、英文、中英混合表格，以及具有不同特征的表格类型，如完整边框、部分边框、无边框、书籍/手册格式、列表、学术论文表格、合并单元格等，还包括低质量和带水印的样本。PaddleOCR-VL 在所有类别中均展现出卓越的性能。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhousetable.png" width="600"/>
</div>
#### 公式
**In-house-Formula**
我们自建的评测集包含简单印刷、复杂印刷、摄像扫描以及手写公式等多种类型。PaddleOCR-VL 在所有类别中均取得了最佳性能。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhouse-formula.png" width="500"/>
</div>
#### 图表
**In-house-Chart 结果比较**
我们自建的评测集涵盖 11 种主要图表类型，包括柱线混合图、饼图、100% 堆叠柱状图、面积图、柱状图、气泡图、直方图、折线图、散点图、堆叠面积图和堆叠柱状图。PaddleOCR-VL 不仅优于专业 OCR VLM 模型，还超越了一些 72B 级别的多模态语言模型。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inhousechart.png" width="400"/>
</div>
## 四、推理部署性能
为了提升PaddleOCR-VL的推理性能，我们在推理工作流程中引入了多线程异步执行。该过程分为三个主要阶段：数据加载（例如，将PDF页面渲染为图像）、布局模型处理和VLM推理——每个阶段都在一个单独的线程中运行。数据通过队列在相邻阶段之间传输，从而实现并发执行以提高效率。在OmniDocBench v1.0数据集上测量了端到端推理速度和GPU使用情况，以512个PDF文件的批次在单个NVIDIA A100 GPU上进行处理。
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/metrics/inference.png" width="600"/>
</div>
> **Notes:** <sup>&#8224;</sup> 表示vLLM后端，<sup>&#8225;</sup> 表示SGLang后端
## 五、可视化
PaddleOCR-VL能够支持多种类型的文档解析，以下是一些预测案例的展示：
### 端到端文档解析
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview1.jpg" width="600"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview2.jpg" width="600"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview3.jpg" width="600"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/overview4.jpg" width="600"/>
</div>
### 文本识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/text_english_arabic.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/text_handwriting_02.jpg" width="300"/>
</div>
### 表格识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/table_01.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/table_02.jpg" width="300"/>
</div>
### 公式识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/formula_EN.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/formula_EN.jpg" width="300"/>
</div>
### 图表识别
<div align="center">
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/chart_01.jpg" width="300"/>
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl/chart_02.jpg" width="300"/>
</div>
## 六、FAQ
**1.如何使用 PaddleOCR-VL 做文档解析 ？**
参考我们的使用文档 [PaddleOCR-VL使用](../../pipeline_usage/PaddleOCR-VL.md)
**2.如何对 PaddleOCR-VL 模型进行微调 ？**
目前我们暂不支持模型的微调，但已经在高优的支持中，即将发布，请保持关注。
**3.为什么我的图表没有识别出来，如何使用图表识别 ？**
因为我们默认图表识别的功能是关闭的，需要手动开启，请参考 [PaddleOCR-VL使用](../../pipeline_usage/PaddleOCR-VL.md), 设置 use_chart_recognition为True 参数来开启。
**4.支持的109种语言有哪些？**
中文、英语、韩语、日语、泰语、希腊语、泰米尔语、泰卢固语
阿拉伯语：阿拉伯语、波斯语、维吾尔语、乌尔都语、普什图语、库尔德语、信德语、俾路支语
拉丁语：法语、德语、南非荷兰语、意大利语、西班牙语、波斯尼亚语、葡萄牙语、捷克语、威尔士语、丹麦语、爱沙尼亚语、爱尔兰语、克罗地亚语、乌兹别克语、匈牙利语、塞尔维亚语（拉丁语）、印度尼西亚语、奥克语、冰岛语、立陶宛语、毛利语、马来语、荷兰语、挪威语、波兰语、斯洛伐克语、斯洛文尼亚语、阿尔巴尼亚语、瑞典语、斯瓦希里语、他加禄语、土耳其语、拉丁语、阿塞拜疆语、库尔德语、拉脱维亚语、马耳他语、巴利语、罗马尼亚语、越南语、芬兰语、巴斯克语、加利西亚语、卢森堡语、罗曼什语、加泰罗尼亚语、盖丘亚语
西里尔文：俄语、白俄罗斯语、乌克兰语、塞尔维亚语（西里尔文）、保加利亚语、蒙古语、阿布哈兹语、阿迪杰语、卡巴尔达语、阿瓦尔语、达尔金语、印古什语、车臣语、拉克语、列兹金语、塔巴萨兰语、哈萨克语、吉尔吉斯语、塔吉克语、马其顿语、鞑靼语、楚瓦什语、巴什基尔语、马里语、摩尔多瓦语、乌德穆尔特语、科米语、奥塞梯语、布里亚特语、卡尔梅克语、图瓦语、萨哈语、卡拉卡尔帕克语
天城语：印地语、马拉地语、尼泊尔语、比哈里语、迈蒂利语、安吉卡语、博杰普里语、马基语、桑塔利语、纽瓦里语、康卡尼语、梵语、哈里亚维语
**5.如果版面检测的结果不理想，有什么方案可以优化？**
由于版面检测主要针对各种文档场景训练，所以您的测试数据如果是非标准文档，如车牌，火车票或身份证图像想做OCR识别，那可以直接使用PaddleOCR-VL-0.9B的模型，通过设置 use_layout_detection 为 False 关闭版面检测模型。如果您发现有任何版面检测的错误，都可以直接尝试一下单独使用PaddleOCR-VL-0.9B的效果。
我们推荐使用 [ERNIEKit 套件](https://github.com/PaddlePaddle/ERNIE/tree/release/v1.4) 对 PaddleOCR-VL-0.9B 模型进行有监督微调（SFT）。具体操作步骤可参考 [ERNIEKit 官方文档](https://github.com/PaddlePaddle/ERNIE/blob/release/v1.4/docs/paddleocr_vl_sft_zh.md)。