DeepSeek-OCR is a high-quality visual language model (VLM) designed for optical character recognition. Given the impressive benchmarks, I recently integrated it into a RAG (Retrieval-Augmented Generation) project aimed at converting complex PDFs into structured Markdown.
While our expectations were high, the transition from benchmark results to a production-ready pipeline revealed some critical practical challenges. Here is a reflection on my experience.
The Technical Setup #
We conducted our experiments on a lab server equipped with an NVIDIA L40s GPU. In order to maximize performance, we utilized Flash Attention 2 and bfloat16 precision.
import torch
from transformers import AutoModel, AutoTokenizer
from concurrent.futures import ProcessPoolExecutor
MODEL_ID = "deepseek-ai/DeepSeek-OCR"
DEVICE = "cuda"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModel.from_pretrained(
MODEL_ID,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map=DEVICE
).eval()Key Implementation Challenges #
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Optimization: A primary hurdle was fine-tuning the hardware-software synergy. We had to carefully balance the inference batch size and process pool workers to ensure the GPU was fully utilized during the preprocessing stages.
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PDF Conversion: Since DeepSeek-OCR is not natively designed for PDFs, we had to convert each page into an image first. We quickly found that image quality directly dictates conversion precision.
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Parameter Sensitivity: We experimented with configurations like lowering
image_sizeto speed up inference. While it reduced processing time, it significantly impacted the model’s accuracy on dense text and complex tables, especially on tables with merged cells, where the model struggled to maintain the correct structural alignment at lower resolutions.
res = model.infer(
tokenizer,
prompt="<image>\n<|grounding|>Convert the document to markdown.",
image_file=tmp_img_path,
output_path=request_output_dir,
eval_mode=True,
base_size=1024,
image_size=640,
crop_mode=True
)The Limitations: Hierarchy and Speed #
The biggest challenge we faced was global hierarchy. Because the model parses documents page-by-page, it captures the internal structure of a single page (headers) excellently, but it lacks a “global view.” It cannot maintain the overall hierarchy of a 50-page document, making it difficult to reconstruct a cohesive flow natively.
Furthermore, inference speed remains a bottleneck. Processing a dataset of roughly 1,680 pages—consisting of papers, journals, and magazines—took approximately 8 hours. For massive document sets, this latency is a significant hurdle.
The Verdict: Precision Over Speed #
Despite these challenges, the output quality was exceptional. When compared to other tools like MinerU, DeepSeek-OCR demonstrated far superior accuracy, particularly with non-English content, where other models frequently hallucinated or made transcription errors.
Conclusion #
DeepSeek-OCR is a powerful tool for high-precision tasks where textual accuracy is non-negotiable. However, for large-scale document pipelines, the trade-off between its visual fidelity and its processing speed requires careful consideration. A hybrid approach that merges its vision with faster structural extractors may be the most viable path for now.
It is worth noting that the recently released DeepSeek-OCR 2 directly addresses the structural and performance hurdles we faced. By introducing “Visual Causal Flow,” the model moves away from rigid grid-scanning toward a human-like “reading order.” This architecture allows it to logically sequence complex layouts and merged cells in tables with far greater stability. While it remains primarily a page-level engine, the significantly cleaner structured Markdown it produces will make reconstructing a global document hierarchy much more achievable.