DeepSeek-OCR-2 official model card provides operating instructions for image document recognition. This article discusses the usage in document organization. It does not publish recognition rate rankings, nor does it understand the sample environment as being able to run on any computer.
First select error-prone pages as samples
To simulate a device information book, the test page should contain double columns, tables, footnotes and cross-page paragraphs. Picking only the neatly typed first page underestimates the subsequent revision work. Record on each page whether words are missing, whether the order is out of place, and whether numbers are split into the wrong lines.
Let the sorting results return to the original page
It is recommended to retain the file name, page number and paragraph identifier when outputting the text. When encountering important parameters, reviewers can quickly compare the pictures; content that cannot be confirmed remains marked for verification without the need for models to supplement it. In particular, original texts such as laws and policies cannot be automatically polished after recognition.
Divide recognition and content rewriting into two steps
The first step is to pursue faithful transcription, and the second step is to summarize or classify within the allowed range. Customers should be able to obtain the original identification results and manual modification records. Model versions, inference dependencies, and page preprocessing methods should also be archived, otherwise it will be difficult to reproduce the problem if differences occur after the upgrade.
Extended reading: AI model.