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PaddleOCR-VL Benchmark Test: Complex Layout Decoding, Who's裸泳?

Comparing PaddleOCR-VL with MinerU2.5, MonkeyOCR, and GPT-4o in handling complex documents. Layout accuracy, hallucination rate, and reading order consistency reveal the details.

The field of document intelligence is bustling with frequent model releases. But in complex scenarios, who can maintain stability?

We took a document mixed with text, tables, formulas, and images and conducted a benchmark test for PaddleOCR-VL, MinerU2.5, MonkeyOCR, and GPT-4o. No parameter comparison, just real-world performance.

![Four comparison images showing benchmark test results. The first one is layout detection, where PaddleOCR-VL accurately identifies text and table areas. The second one shows formula and image processing, with competitors having red highlights for errors. The third one uses numbers to demonstrate reading order consistency. The fourth one is a hallucination rate chart, with PaddleOCR-VL having the lowest error rate.](https://wink.run/image?url=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FG30tV08WwAAKJL7%3Fformat%3Djpg%26name%3Dlarge)

![Four comparison images showing benchmark test results. The first one is layout detection, where PaddleOCR-VL accurately identifies text and table areas. The second one shows formula and image processing, with competitors having red highlights for errors. The third one uses numbers to demonstrate reading order consistency. The fourth one is a hallucination rate chart, with PaddleOCR-VL having the lowest error rate.](https://wink.run/image?url=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FG30tV05X0AIY2gm%3Fformat%3Djpg%26name%3Dlarge)

The results are clear. PaddleOCR-VL is solid in layout detection, no missed elements, no misclassification. Reading order is coherent, and hallucination content is controlled. Other models have their own ways of failing: missed recognition, wrong classification, messy order, or even creating things out of thin air.

Some users mentioned support for complex scripts like Arabic and Japanese. The official response included test images.

![Image containing handwritten notes in English and Arabic. On the left is blue ink on green-lined paper, and on the right is printed text about OCR technology's progress in multilingual recognition.](https://wink.run/image?url=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FG32OBcCXQAAsmEE%3Fformat%3Djpg%26name%3Dlarge)

![Image showing three columns of text in Russian, Japanese, and Chinese. The Russian section lists the founding dates of geographical societies in various countries, and the Japanese section discusses geographical literature.](https://wink.run/image?url=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FG32OBcJXMAAQ4Ao%3Fformat%3Djpg%26name%3Dlarge)

It seems to have targeted optimizations for right-to-left text and mixed layouts. However, some users reported issues when running on Linux with an NVIDIA RTX 4080, so real-world deployment still involves its own pitfalls.

Tool updates often follow this pattern: plenty of highlights in promotions, but details determine success in real use. PaddleOCR-VL's benchmark test data is solid this time, but whether it's worth it depends on your specific document type and system environment.

Next time will be about multilingual text recognition, continuing to monitor real-world performance.

发布时间: 2025-10-22 08:12