Handwriting Recognition Guide

A skill for applying handwriting text recognition (HTR) to digitize historical documents, archival manuscripts, and handwritten research notes. Covers HTR platforms, image preprocessing, model training, post-correction, and integration into digital humanities research workflows.

Handwriting Recognition vs. Printed OCR

Key Differences

Printed Text OCR:
  - Characters are standardized and uniform
  - Well-solved problem (>99% accuracy on clean scans)
  - Tools: Tesseract, ABBYY FineReader, Adobe Acrobat

Handwriting Text Recognition (HTR):
  - Characters vary by writer, mood, pen, era
  - Much harder -- typically 85-95% character accuracy
  - Requires training on specific handwriting styles
  - Tools: Transkribus, Kraken, HTR-Flor, Google Cloud Vision

Challenges specific to historical documents:
  - Faded ink, bleed-through, stains, tears
  - Archaic letterforms and abbreviations
  - Multiple hands in one document
  - Non-standard orthography
  - Mixed languages and scripts

HTR Platforms

Transkribus (State of the Art for Historical Documents)

Pricing note: Transkribus uses a credit-based pricing model. A limited free tier is available, but processing large volumes of pages requires purchasing credits.

Transkribus is the leading platform for historical HTR.

Workflow:
  1. Upload document images
  2. Automatic layout analysis (detect text regions and baselines)
  3. Manual correction of layout (if needed)
  4. Apply a pre-trained HTR model (or train your own)
  5. Review and correct transcription
  6. Export as TEXT, PAGE XML, TEI, DOCX, or PDF

Pre-trained models:
  - Noscemus GM (general model for Latin scripts)
  - English Writing M1 (18th-19th century English)
  - German Kurrent models
  - Dutch, French, Italian, Spanish models available

Training a custom model:
  - Requires ~15,000-25,000 words of ground truth (manually transcribed)
  - Can start with a pre-trained base model and fine-tune
  - Training takes 1-8 hours depending on dataset size

Other Tools

Tool	Type	Strengths
Transkribus	Cloud platform	Best for historical documents, active community
Kraken	Open source (Python)	Flexible, scriptable, custom training
eScriptorium	Open source (web)	Based on Kraken, collaborative interface
Google Cloud Vision	API	Good for modern handwriting, many languages
Azure AI Vision	API	Competitive with Google for modern text
HTR-Flor	Open source	Research-focused, PyTorch-based

Image Preprocessing

Preparing Scans for HTR

from PIL import Image, ImageFilter, ImageEnhance


def preprocess_document_image(image_path: str,
                               output_path: str) -> dict:
    """
    Preprocess a document scan for optimal HTR performance.

    Args:
        image_path: Path to the input scan
        output_path: Path to save the preprocessed image
    """
    img = Image.open(image_path)

    # Convert to grayscale
    img = img.convert("L")

    # Enhance contrast
    enhancer = ImageEnhance.Contrast(img)
    img = enhancer.enhance(1.5)

    # Remove noise
    img = img.filter(ImageFilter.MedianFilter(size=3))

    # Binarize (convert to black and white)
    threshold = 128
    img = img.point(lambda x: 255 if x > threshold else 0, "1")

    img.save(output_path)

    return {
        "original": image_path,
        "processed": output_path,
        "steps_applied": [
            "Grayscale conversion",
            "Contrast enhancement (1.5x)",
            "Median filter (noise removal)",
            "Binarization (threshold=128)"
        ],
        "additional_steps_if_needed": [
            "Deskewing (correct rotation)",
            "Dewarping (correct page curvature)",
            "Bleed-through removal",
            "Background normalization"
        ]
    }

Scanning Best Practices

Resolution:    300-400 DPI for most documents
               600 DPI for fine handwriting or damaged originals
Color:         Grayscale usually sufficient; color for illuminated MSS
Format:        TIFF (lossless) for archival; PNG for working copies
Lighting:      Even, diffused light; avoid shadows and glare
Flatness:      Use a book cradle or V-shaped scanner for bound volumes
Calibration:   Include a color/grayscale chart for batch consistency

Post-OCR Correction

Semi-Automated Correction Workflow

def post_correction_workflow(raw_transcription: str,
                              dictionary: set,
                              confidence_threshold: float = 0.8) -> dict:
    """
    Post-correction strategy for HTR output.

    Args:
        raw_transcription: Raw OCR/HTR text output
        dictionary: Set of valid words for the document's language/period
        confidence_threshold: Below this, flag for manual review
    """
    words = raw_transcription.split()
    flagged = []
    corrected = []

    for word in words:
        clean = word.strip(".,;:!?()[]")
        if clean.lower() in dictionary:
            corrected.append(word)
        else:
            flagged.append({
                "word": word,
                "position": len(corrected),
                "suggestion": "Manual review needed"
            })
            corrected.append(word)

    return {
        "total_words": len(words),
        "flagged_words": len(flagged),
        "estimated_accuracy": 1 - len(flagged) / max(len(words), 1),
        "flagged": flagged[:20],
        "correction_strategies": [
            "Dictionary-based spell checking (period-appropriate dictionary)",
            "N-gram language model for context-aware correction",
            "Crowdsourcing (Zooniverse, FromThePage)",
            "Double-keying (two independent transcribers, compare)",
            "AI-assisted correction with human verification"
        ]
    }

Integration with Research Workflows

From Transcription to Analysis

1. Transcribe documents using HTR
2. Correct and validate transcriptions
3. Encode in TEI-XML for digital editions
4. Apply NLP for named entity recognition, topic modeling
5. Link entities to knowledge bases (Wikidata, VIAF)
6. Publish as a searchable digital archive

Tools for TEI encoding:
  - oXygen XML Editor (standard for digital humanities)
  - TEI Publisher (web-based publishing platform)
  - FromThePage (collaborative transcription with TEI export)

Evaluating HTR Accuracy

Report Character Error Rate (CER) and Word Error Rate (WER) on a held-out test set. CER below 5% is generally considered production-quality for historical documents. Always compare against a manually created ground truth. Report accuracy separately for different document types, hands, or time periods if your corpus is heterogeneous.