Update README.md

README.md

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 ## News
 
-- [2026-05-11] Released flagship document parsing models: [Infinity-Parser2-Pro](https://huggingface.co/infly/Infinity-Parser2-Pro), [Infinity-Parser2-Flash](https://huggingface.co/infly/Infinity-Parser2-Flash), and the dataset [Infinity-Doc2-5M](https://huggingface.co/datasets/infly/Infinity-Doc2-5M). [Infinity-Parser2](Infinity-Parser2) achieves SOTA results on olmOCR-bench and ParseBench.
+- [2026-05-11] Released flagship document parsing models: [Infinity-Parser2-Pro](https://huggingface.co/infly/Infinity-Parser2-Pro), [Infinity-Parser2-Flash](https://huggingface.co/infly/Infinity-Parser2-Flash), and the dataset [Infinity-Doc2-5M](https://huggingface.co/datasets/infly/Infinity-Doc2-5M). Infinity-Parser2 achieves SOTA results on olmOCR-bench and ParseBench.
 
 <p align="center">
-    <img src="assets/example_1.png" width="1600"/>
-<p>
-  
+    <img src="https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/newspaper_1.png" width="1200"/>
+</p>
+
 ## Introduction
 
 We are excited to release Infinity-Parser2, our latest flagship document understanding model. We offer two distinct variants to address diverse deployment constraints: Infinity-Parser2-Pro, optimized for maximum accuracy in precision-critical tasks, achieves state-of-the-art results on olmOCR-Bench (87.6%) and ParseBench (74.3%), surpassing frontier models including DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU-2.5. Infinity-Parser2-Flash, engineered for low-latency inference, delivers a 3.68x speedup over our previous Infinity-Parser-7B model. With significant upgrades to both our data engine and multi-task reinforcement learning approach, the model consolidates robust multi-modal parsing capabilities into a unified architecture, unlocking brand-new zero-shot capabilities across a wide range of real-world business scenarios.
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+  
 ### Key Features
 
 - **Upgraded Data Engine**: We have comprehensively enhanced our synthetic data engine to support both fixed-layout and flexible-layout document formats. By curating nearly 5 million diverse document parsing samples across a wide range of layouts, combined with a dynamic adaptive sampling strategy, we ensure highly balanced and robust multi-task learning across various document types.
@@ -531,6 +531,18 @@ vllm serve infly/Infinity-Parser2-Pro \
 
 For more details, please refer to the [official guide](https://github.com/infly-ai/INF-MLLM/blob/main/Infinity-Parser2).
 
+### Visual Parsing Examples
+
+| Visualization | Note |
+| --- | --- | 
+| [A-Stock](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/a_stock.png) | Easy to miscount colspan in tables |
+| [Multi-Column Layout](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/muti_column.png) | Complex layout analysis and reading order recovery. |
+| [Historical Newspaper](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/newspaper_2.png) | High probability of bounding box omission caused by ultra-dense text distribution, narrow column margins, and microscopic fonts. |
+| [US-Stock](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/us_stock.png) | Precise row alignment across wide frameless spaces and capturing the hierarchical semantics of indented headers. |
+| [Academic Paper (arXiv)](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/arxiv.png) | Accurate structural preservation of complex multi-line mathematical formulas, dense inline notations, and deeply nested subscripts/superscripts. |
+| [Magazine Page](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/magazine.png) | Complex reading order recovery in an asymmetric multi-column layout. |
+| [Scanned Mathematics](https://raw.githubusercontent.com/infly-ai/INF-MLLM/main/Infinity-Parser2/assets/old_scan_math.png) | Degraded and blurred print |
+
 ## Limitations
 
 Infinity-Parser2 has several known limitations to consider. It primarily supports English and Chinese documents, and performance degrades when processing multilingual content. Accuracy may also be reduced when parsing charts with complex layouts, as well as documents containing multi-oriented elements such as table rotated at varying angles. Additionally, the model does not capture fine-grained text formatting (e.g., bold, italic, strikethrough) and exhibits suboptimal multimodal instruction-following capability, meaning it may not always reliably follow complex multi-step visual instructions.