Paddle OCR-VL & DeepSeek-OCR

#deepseek #ocr #llm

PaddleOCR-VL

What it is

PaddleOCR-VL is a vision-language model (VLM) built for document parsing-meaning not just "read the text"
but "understand the layout, tables, formulas, charts, multilingual text." it is supports 109 languages.

Why it stands out

Multilingual: covers a wide spectrum of scripts (English, French, german, etc.).
Document-layout aware: recognizes text blocks, tables, formulas, charts so it is designed for complex documents beyond simple printed text.
Resource efficiency: the documentation claims faster inference, lower memory consumption compared with many generic large VLMs.

Where to use it

Parsing scanned PDF documents where you need structure and multiple languages.
layout understanding is required (not just OCR, but "what is in the table?").

Limitations

although it supports many languages, real-world accuracy might vary wildly for less common scripts or poor quality scans.
as with any model: OCR + layout parsing is never "perfect" - expect error rates, need post-processing.

DeepSeek-OCR

What it is

Deepseek-OCR is a newer open-source model that targets OCR/document parsing—but with a twist:
they emphasize "vision-text compression" (how many vision tokens are needed to decode text tokens)
document throughput/efficiency.

Why it stands out

Code and weights appear publicly available (huggingFace).
Easy to set up.

Where it could excel

If you care about throughput—running large volumes of pages/documents, wanting to minimize compute per page.
Use-cases where you have relatively "standard" scanned documents and you want a streamlined/inexpensive pipeline.
Potentially situations where compressing document representations matters (archive, storage, indexing).

Limitations

Since it is newer: less maturity, fewer benchmarks publicly compared to paddleOCR-vl.
Compression focus may trade off accuracy in edge cases (complex layout, unusual fonts, low quality scans).
Documentation & ecosystem may be thinner compared to more established toolkits.