AI Tips: Using Qwen-VL-Max for Chinese OCR of Classical Books

Most AI Models Struggle with OCR for Classical Chinese Texts—But Qwen VL Max Shows Promise

Optical Character Recognition (OCR) remains one of the most difficult tasks for AI when it comes to classical Chinese texts—especially those written in vertical, top-to-bottom and right-to-left formats. This difficulty is further compounded when the source material is handwritten or uses classical fonts.

In China, several specialized OCR companies have emerged that focus specifically on classical Chinese prints. These firms often rely on proprietary models that deliver high accuracy, but their services can be quite costly. Based on my own experience, I’ve spent between $200 and $300 using one such provider.

Is there an alternative? Fortunately, the answer is yes—and potentially a game-changer. With recent advances in AI, models like Qwen VL Max are beginning to perform impressively in this niche domain. You can explore its capabilities on the official testing page: https://huggingface.co/spaces/Qwen/Qwen-VL-Max

To illustrate, I tested the model on an image from the Chinese imperial civil service examination. While the model initially failed, it achieved near-perfect accuracy once prompted with clear instructions—specifically, to interpret the text vertically and read from right to left. This kind of instruction-based adaptability is crucial when working with complex historical texts.

I'm currently working on an automated solution to integrate Qwen’s OCR functionality via API, and I plan to publish the code on GitHub. However, it's worth noting that the Qwen API is currently not very accessible to users based in the United States, which could be a limitation for Western researchers.

Nevertheless, the potential here is enormous. A capable and low-cost alternative to traditional OCR services could significantly reduce the barrier to entry for digital humanities projects focused on Chinese history and literature.