From Deep Learning to Large‑Model OCR: Which Model Leads the Pack?

OCR Model Technology History

OCR (Optical Character Recognition) began as the first technology enabling machines to read text, initially applied to handwritten digit recognition using CNN and LSTM architectures (CRNN, CTC). Early OCR 1.0 systems consisted of two modules: text detection (locating text regions) and text recognition (converting regions to editable text). Applications included bank ticket automation, ID extraction, document digitization, and real‑time camera translation.

OCR 2.0: Semantic Structure Recognition

As information formats diversified, plain text was no longer sufficient. Traditional OCR could read characters but could not understand layout semantics (titles, tables, formulas). OCR 2.0 introduced Vision Transformers, layout analysis, and vision‑language alignment, enabling models to output structured Markdown, HTML, or JSON with table, formula, and graphic relationships. Representative models include Microsoft LayoutLM, Baidu PaddleOCR 2.0, and DeepSeek‑OCR.

VLM: Multimodal Vision‑Language Models

Since 2023, large‑scale multimodal models such as GPT‑4V, Gemini, Qwen‑VL, and InternVL have mapped images and text into a shared semantic space, allowing “image‑text dual mastery”. While generic VLMs excel at visual understanding, they can be slow and less precise on fine‑grained OCR tasks, prompting the development of specialized VLM‑fine‑tuned OCR models.

Major Open‑Source VLMs

InternVL 3.5

https://github.com/OpenGVLab/InternVL

(2025) offers 8B‑40B parameters, improves image understanding, table parsing, and cross‑modal retrieval, and introduces Cascade RL to stabilize multi‑step reasoning. It delivers strong performance on chart Q&A and scientific paper analysis but requires substantial GPU memory.

Qwen3‑VL

https://github.com/QwenLM/Qwen3-VL

(2025) spans 3B‑72B parameters, supports object detection, chart comprehension, and video analysis. It excels in cross‑language document parsing and long‑video understanding, yet larger variants incur high inference latency and GPU demand.

Open‑Source OCR Models

DeepSeek‑OCR

https://github.com/deepseek-ai/DeepSeek-OCR

uses a visual‑text compression architecture with a DeepEncoder (window attention → 16× convolutional compressor → CLIP‑large) and an MoE decoder (based on DeepSeek‑3B‑MoE). It compresses each page to 256 tokens, reduces memory usage >10×, and maintains >97% accuracy, making it ideal for long‑document and multi‑page batch processing.

PaddleOCR

https://github.com/PaddlePaddle/PaddleOCR

follows a mature two‑stage pipeline (detection → recognition) with diverse detectors (DB, EAST, SAST) and recognizers (CRNN, SVTR, PP‑OCRv4). Its strength lies in extensive vertical‑scene adapters (tables, receipts, handwriting) and a full toolchain from data annotation to multi‑platform deployment.

MonkeyOCR

https://github.com/Yuliang-Liu/MonkeyOCR

introduces a Structure‑Recognition‑Relation (SRR) triple design. It first applies DocLayout‑YOLO for layout detection, then a lightweight LLM for text block recognition, and finally predicts logical relations among blocks. This balances pipeline and end‑to‑end approaches, runs efficiently on a single RTX 3090, and excels in complex layout parsing.

OCR Large‑Model Application Guide: Three Key Scenarios

Long‑Document Processing

For contracts, financial reports, and legal documents, DeepSeek‑OCR shines. In a 158‑page M&A contract with extensive annotations, it achieved an annotation‑association accuracy of 89.5%, a 27‑point gain over Tesseract 5.0. Its visual‑text compression preserves document continuity, avoiding context breaks.

Academic Papers and Educational Materials

MonkeyOCR excels at formula extraction: on a 62‑page Nature article containing 45 complex formulas, it reached 92.1% formula‑recognition accuracy, outputting LaTeX ready for use. DeepSeek‑OCR complements by handling cross‑references, citations, and terminology, making it suitable for building academic knowledge bases.

Edge Computing and Lightweight Scenarios

MonkeyOCR’s dynamic attention runs on a Raspberry Pi 4B using only 35% memory and on Jetson AGX Xavier handling four simultaneous camera streams, fitting smart‑retail and industrial inspection. PaddleOCR’s lightweight mobile models infer within 100 ms on Android/iOS, ideal for ID, bank‑card, and license‑plate recognition.

Conclusion and Outlook

The article systematically reviews OCR’s trajectory: from CNN+LSTM‑based OCR 1.0, through ViT‑enabled OCR 2.0 with layout analysis, to the current multimodal VLM era. Detailed analyses of DeepSeek‑OCR, PaddleOCR, and MonkeyOCR are provided, along with best‑practice recommendations for long‑document handling, academic digitization, and edge deployment. Future OCR will deepen multimodal fusion and end‑to‑end structured understanding, while lightweight optimizations will broaden its presence on edge devices, cementing OCR as the “eyes” of large‑model perception.