OCR Models Guide: DeepSeek, PaddlePaddle, Others for High Accuracy & Local Deployment

In the era of digital transformation, vast amounts of information remain in images, scans, PDFs, and handwritten documents. Optical Character Recognition (OCR) aims to convert these visual contents into editable, searchable text, and modern OCR has evolved from template‑based methods to deep‑learning‑driven end‑to‑end neural systems.

GLM‑OCR

GLM‑OCR, released by Zhipu AI in February 2026, is a 0.9B lightweight multimodal OCR model designed for complex document scenarios. It handles mixed printed and handwritten text, multilingual content, merged table cells, mathematical formulas, and seals. The model runs on as little as 4 GB GPU memory, making it suitable for consumer‑grade GPUs and edge devices, and supports private local deployment. In the OmniDocBench V1.5 benchmark, GLM‑OCR achieved a score of 94.62, comparable to Gemini‑3‑Pro, and is applicable to office documents, academic formulas, government and financial file verification, and code snippet extraction.

Online demo:

https://go.hyper.ai/TUpFZ

PaddleOCR‑VL‑1.5

PaddleOCR‑VL‑1.5, part of the PaddleOCR series released in January 2026, extends OCR capabilities to complex layouts such as invoices, contracts, and scanned papers. Integrated with vLLM’s OpenAI‑compatible interface, the tutorial demonstrates a full pipeline from image upload to recognition results. With only 0.9 B parameters, it reaches 94.5% accuracy on OmniDocBench V1.5, while adding support for seal detection and text localization.

Online demo:

https://go.hyper.ai/hSkP2

LightOnOCR‑2‑1B

LightOnOCR‑2‑1B, announced by LightOn AI in January 2026, is a 1 B‑parameter end‑to‑end OCR model that unifies document understanding and text generation using a Vision‑Language Transformer. Trained with Reinforcement Learning from Visual Rationale (RLVR), it runs on consumer GPUs with ~6 GB memory. The model excels in handling complex documents, handwritten text, and LaTeX formulas.

Online demo:

https://go.hyper.ai/iCoBb

DeepSeek‑OCR 2 (Visual Causal Flow)

DeepSeek‑OCR 2, released by the DeepSeek team in January 2026, introduces the DeepEncoder V2 architecture, causal flow queries, and dual‑stream attention to reorder visual tokens dynamically, enabling more accurate reconstruction of natural reading order in complex documents. On OmniDocBench V1.5 it scored 91.09%, a notable improvement over its predecessor while reducing result duplication.

Online demo:

https://go.hyper.ai/3zJmi

MonkeyOCR

MonkeyOCR, open‑sourced by Huazhong University of Science and Technology together with Kingsoft Office in June 2025, follows a structure‑recognition‑relation triple paradigm. It converts unstructured documents into structured information through precise layout analysis, content recognition, and logical ordering. Compared with traditional pipelines, MonkeyOCR improves overall performance by 5.1%, with 15.0% and 8.6% gains on formula and table parsing respectively, and processes up to 0.84 pages per second.

Online demo: https://go.hyper.ai/ywJjv All the models above are available for local deployment, requiring modest GPU memory, and the article provides direct links to run each model online, facilitating hands‑on experimentation.