OAR-OCR

A native Rust toolkit for OCR, document layout analysis, and vision-language document understanding.

Highlights

• End-to-end text detection and recognition with PP-OCR models, including PP-OCRv6.
• Document structure analysis for layout, tables, formulas, seals, orientation, and rectification.
• Native Candle inference for compact document VLMs through the oar-ocr-vl crate.
• CPU and GPU execution, model auto-download, and in-memory ONNX model loading.

Quick Start

Installation

cargo add oar-ocr

The default build enables ONNX Runtime binary downloads and SIMD acceleration. Add only the optional capabilities needed by your application. For example:

cargo add oar-ocr --features cuda,auto-download

This keeps the default download-binaries and simd features enabled, makes the ONNX Runtime CUDA execution provider available for selection, and downloads missing registered model files from ModelScope into $OAR_HOME when they are first used.

See the Cargo feature guide for all available features and the model guide for model download and cache behavior.

Builders also accept raw ONNX bytes such as include_bytes!, allowing models to be embedded in a single binary. See Loading Models from Memory.

OCR Pipeline

With auto-download, pass registered model names directly. Otherwise, replace them with local paths.

use oar_ocr::domain::tasks::TextDetectionConfig;
use oar_ocr::prelude::*;
use std::path::Path;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let ocr = OAROCRBuilder::new(
        "pp-ocrv6_tiny_det.onnx",
        "pp-ocrv6_tiny_rec.onnx",
        "ppocrv6_tiny_dict.txt",
    )
    .text_detection_config(TextDetectionConfig {
        score_threshold: 0.2,
        box_threshold: 0.45,
        unclip_ratio: 1.4,
        max_candidates: 3000,
        ..Default::default()
    })
    .build()?;

    let image = load_image(Path::new("document.jpg"))?;
    let results = ocr.predict(vec![image])?;

    for region in &results[0].text_regions {
        if let Some((text, confidence)) = region.text_with_confidence() {
            println!("{text} ({confidence:.2})");
        }
    }

    Ok(())
}

Document Structure Analysis

use oar_ocr::prelude::*;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let structure = OARStructureBuilder::new("pp-doclayout_plus-l.onnx")
        .with_table_classification("pp-lcnet_x1_0_table_cls.onnx")
        .with_table_structure_recognition("slanet_plus.onnx", "wireless")
        .table_structure_dict_path("table_structure_dict_ch.txt")
        .with_ocr(
            "pp-ocrv5_mobile_det.onnx",
            "pp-ocrv5_mobile_rec.onnx",
            "ppocrv5_dict.txt",
        )
        .build()?;

    let result = structure.predict("document.jpg")?;
    println!("{}", result.to_markdown());

    Ok(())
}

Supported Models

The classic pipeline runs ONNX models through ONNX Runtime and supports the following model families. See the pre-trained model guide for exact checkpoints, dictionaries, download links, and auto-download names.

Classic ONNX Models

Task	Supported model families
Text detection	PP-OCRv4, PP-OCRv5, and PP-OCRv6 DB detectors
Text recognition	PP-OCRv3, PP-OCRv4, PP-OCRv5, PP-OCRv6, SVTRv2, and RepSVTR CTC recognizers
Document preprocessing	PP-LCNet document orientation, PP-LCNet text-line orientation, and UVDoc rectification
Layout detection	PicoDet, RT-DETR-H, PP-DocLayout S/M/L, PP-DocLayout Plus-L, PP-DocLayoutV2/V3, and PP-DocBlockLayout
Table analysis	PP-LCNet table classification, RT-DETR-L cell detection, and SLANet, SLANet+, and SLANeXt structure recognition
Formula recognition	PP-FormulaNet, PP-FormulaNet Plus, and UniMERNet
Seal text detection	PP-OCRv4 mobile and server seal detectors

Available text-recognition checkpoints cover Chinese, Traditional Chinese, English, Arabic, Cyrillic, Devanagari, Greek, Eastern Slavic, Japanese, Georgian, Korean, Latin, Tamil, Telugu, and Thai scripts or languages.

Vision-Language Models

The oar-ocr-vl crate provides native Candle inference for compact document VLMs on CPU, CUDA, and Metal.

Model	Parameters	Capabilities
PaddleOCR-VL	0.9B	Page parsing, text, table, formula, and chart recognition
PaddleOCR-VL-1.5	0.9B	PaddleOCR-VL tasks plus text spotting and seal recognition
PaddleOCR-VL-1.6	0.9B	Region-aware page parsing and task-specific recognition
GLM-OCR	0.9B	Page parsing, text, table, and formula recognition
OvisOCR2	0.8B	Model-native full-page document-to-Markdown parsing
MonkeyOCRv2-S-Parsing	0.6B	Model-native layout, end-to-end parsing, text, formula, and OTSL-table recognition
MonkeyOCRv2-B-Parsing	0.7B	Higher-capacity ViT-B variant with the same parsing and recognition tasks
HunyuanOCR 1.5 / 1.0	1B	Prompt-driven full-page parsing, text spotting, table, formula, and chart recognition, with optional DFlash decoding for 1.5
MinerU2.5-2509	1.2B	Model-native two-step layout detection and content extraction
MinerU2.5-Pro-2605	1.2B	Newer MinerU2.5 checkpoint using the same two-step pipeline
MinerU-Diffusion-V1-0320	2.5B	Block-diffusion OCR with structured two-step extraction or single-pass text recognition

PaddleOCR-VL variants and GLM-OCR integrate with the external-layout DocParser. OvisOCR2 and the MonkeyOCRv2 S/B parsing models instead provide model-native full-page paths through dedicated examples. HunyuanOCR and the MinerU models also use their model-native parsing pipelines.

See the oar-ocr-vl guide for setup and oar-ocr-vl/examples for runnable examples.

Documentation

• Usage guide — APIs, builder patterns, accelerators, and model loading
• Cargo features — defaults, execution providers, and feature combinations
• Pre-trained models — model files, dictionaries, and auto-download behavior
• Environment variables — runtime and performance overrides
• FAQ — common build and runtime issues

Examples

See the usage guide for other pipeline configurations and APIs. Complete classic-pipeline examples live in examples, while VLM examples live in oar-ocr-vl/examples.

Acknowledgments

This project builds upon the excellent work of several open-source projects:

• ort: Rust bindings for ONNX Runtime by pykeio. This crate provides the Rust interface to ONNX Runtime that powers the efficient inference engine in this OCR library.

• PaddleOCR: Baidu's awesome multilingual OCR toolkits based on PaddlePaddle. This project utilizes PaddleOCR's pre-trained models, which provide excellent accuracy and performance for text detection and recognition across multiple languages.

• Candle: A minimalist ML framework for Rust by Hugging Face. We use Candle to implement Vision-Language model inference.