RapidOCR Extractor

Extractor ID: ocr-rapidocr

Category: OCR Extractors

Overview

The RapidOCR extractor performs optical character recognition on image files using the RapidOCR library with ONNX Runtime. It provides fast, accurate OCR without requiring external services or GPU acceleration.

RapidOCR is built on ONNX Runtime and uses optimized OCR models for efficient text detection and recognition. It’s ideal for processing image corpora where embedded text needs to be extracted for search or analysis.

Installation

RapidOCR is an optional dependency:

pip install "biblicus[ocr]"

This installs rapidocr-onnxruntime which includes all necessary models and the ONNX Runtime.

Supported Media Types

  • image/png - PNG images

  • image/jpeg - JPEG/JPG images

  • image/gif - GIF images

  • image/bmp - BMP images

  • image/tiff - TIFF images

  • image/webp - WebP images

Only image media types are processed. Other media types are automatically skipped.

Configuration

Config Schema

class RapidOcrExtractorConfig(BaseModel):
    min_confidence: float = 0.5  # Minimum confidence threshold (0.0-1.0)
    joiner: str = "\n"            # String to join recognized lines

Configuration Options

Option

Type

Default

Description

min_confidence

float

0.5

Minimum per-line confidence to include (0.0-1.0)

joiner

str

"\n"

String used to join recognized text lines

Usage

Command Line

Basic Usage

# Extract text from images
biblicus extract my-corpus --extractor ocr-rapidocr

Custom Configuration

# Higher confidence threshold
biblicus extract my-corpus --extractor ocr-rapidocr \
  --config min_confidence=0.75

# Use space as joiner instead of newline
biblicus extract my-corpus --extractor ocr-rapidocr \
  --config joiner=" "

Configuration File

extractor_id: ocr-rapidocr
config:
  min_confidence: 0.6
  joiner: "\n"

biblicus extract my-corpus --configuration configuration.yml

Python API

from biblicus import Corpus

# Load corpus
corpus = Corpus.from_directory("my-corpus")

# Extract with defaults
results = corpus.extract_text(extractor_id="ocr-rapidocr")

# Extract with custom config
results = corpus.extract_text(
    extractor_id="ocr-rapidocr",
    config={
        "min_confidence": 0.7,
        "joiner": " "
    }
)

In Pipeline

OCR Fallback

extractor_id: pipeline
config:
  stages:
    - extractor_id: pass-through-text
    - extractor_id: ocr-rapidocr
    - extractor_id: select-text

Media Type Routing

extractor_id: select-smart-override
config:
  default_extractor: pass-through-text
  overrides:
    - media_type_pattern: "image/.*"
      extractor: ocr-rapidocr

Examples

Screenshot Collection

Extract text from screenshots:

biblicus extract screenshots --extractor ocr-rapidocr

Scanned Documents

Process scanned document images:

biblicus extract scans --extractor ocr-rapidocr \
  --config min_confidence=0.7

Document Photos

Extract text from photos of documents:

from biblicus import Corpus

corpus = Corpus.from_directory("document-photos")

results = corpus.extract_text(
    extractor_id="ocr-rapidocr",
    config={"min_confidence": 0.6}
)

High-Confidence Extraction

Only include very confident results:

biblicus extract images --extractor ocr-rapidocr \
  --config min_confidence=0.9

Confidence Scores

RapidOCR provides per-line confidence scores:

  • Confidence Range: 0.0 to 1.0

  • Default Threshold: 0.5 (50%)

  • Returned Confidence: Average of accepted lines

The extractor:

  1. Recognizes text lines with individual confidence scores

  2. Filters lines below min_confidence threshold

  3. Returns the average confidence of accepted lines

Interpreting Confidence

  • 0.9-1.0: Excellent recognition

  • 0.7-0.9: Good recognition

  • 0.5-0.7: Acceptable recognition

  • 0.0-0.5: Poor recognition (filtered by default)

Performance

  • Speed: Fast (0.5-2 seconds per image)

  • Memory: Moderate (models loaded once)

  • Accuracy: Good for clear text, moderate for degraded images

RapidOCR is significantly faster than VLM approaches while maintaining good accuracy for standard OCR tasks.

Error Handling

Missing Dependency

If RapidOCR is not installed:

ExtractionRunFatalError: RapidOCR extractor requires an optional dependency.
Install it with pip install "biblicus[ocr]".

Non-Image Items

Non-image items are silently skipped (returns None).

No Text Recognized

Images without recognizable text produce empty extracted text and are counted in extracted_empty_items.

Per-Item Errors

Processing errors for individual images are recorded but don’t halt extraction.

Use Cases

Screenshot Archives

Extract text from UI screenshots:

biblicus extract screenshots --extractor ocr-rapidocr

Scanned Document Collections

Process scanned paper documents:

biblicus extract scans --extractor ocr-rapidocr

Photo Documentation

Extract text from photos of documents or signs:

biblicus extract photos --extractor ocr-rapidocr

Mixed Media Pipeline

Combine with other extractors:

extractor_id: pipeline
config:
  stages:
    - extractor_id: pass-through-text
    - extractor_id: pdf-text
    - extractor_id: ocr-rapidocr
    - extractor_id: select-text

When to Use RapidOCR vs Alternatives

Use RapidOCR when:

  • Images contain primarily text

  • You need fast, local OCR

  • Text is reasonably clear

  • No GPU is required

Use PaddleOCR-VL when:

  • Text is in CJK languages (Chinese, Japanese, Korean)

  • You need better accuracy for complex layouts

  • API-based processing is acceptable

Use VLM extractors when:

  • Images have complex layouts

  • You need document understanding beyond text

  • Tables, equations, or diagrams are present

  • Highest accuracy is critical

Use text extractors when:

  • Documents have embedded text layers

  • PDFs are born-digital (not scanned)

  • You want instant extraction

Best Practices

Tune Confidence Threshold

Test different thresholds on sample images:

# Try different confidence levels
biblicus extract test-images --extractor ocr-rapidocr \
  --config min_confidence=0.7

Monitor Confidence Scores

Check average confidence in results:

results = corpus.extract_text(extractor_id="ocr-rapidocr")
# Confidence is available in extraction metadata

Use for Clear Text

RapidOCR works best with:

  • Clear, high-resolution images

  • Good lighting/contrast

  • Standard fonts

  • Horizontal text orientation

Consider Alternatives for:

  • Very low quality images

  • Complex multi-column layouts

  • Mixed text/graphics

  • Rotated or skewed text

Image Quality Tips

For best OCR results:

  • Resolution: 300+ DPI preferred

  • Contrast: High contrast between text and background

  • Clarity: Sharp focus, not blurry

  • Orientation: Straight, not skewed

  • Lighting: Even illumination

See Also