The article explains how optical character recognition (OCR) models, like deepseek-ocr, process images of text into machine-readable formats. It details the roles of the encoder and decoder in transforming visual data into structured text while highlighting the advancements in learning techniques that reduce the need for manual coding.

The olmOCR-2-7B-1025 model is a fine-tuned version of Qwen2.5-VL-7B-Instruct, designed to enhance optical character recognition (OCR) capabilities, especially for complex cases like math equations and tables. It is recommended to use the FP8 version for practical applications and can handle large-scale document processing through the olmOCR toolkit. The model demonstrates high performance on various OCR benchmarks.