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From ra-mcp-tools
Transcribes handwritten historical documents using HTRflow MCP tools. Provides interactive viewer artifact, per-line JSON transcriptions, and archival exports.
npx claudepluginhub ai-riksarkivet/ra-mcp --plugin ra-mcp-toolsHow this skill is triggered — by the user, by Claude, or both
Slash command
/ra-mcp-tools:htr-transcriptionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Transcribe handwritten historical documents using the HTRflow MCP server.
Translate visa application documents (images) to English and create a bilingual PDF with original and translation
Translates visa application document images to English via OCR (macOS Vision, EasyOCR, Tesseract), auto-rotates via EXIF, and generates bilingual A4 PDFs with original image and formatted translation. For bank/employment certificates.
Parses complex documents with PaddleOCR to extract text, tables, formulas, charts, and layout structure. Use for invoices, academic papers, multi-column layouts, or any document needing structured understanding.
Share bugs, ideas, or general feedback.
Transcribe handwritten historical documents using the HTRflow MCP server. Returns an interactive viewer, per-line transcription JSON, and archival exports.
htr_transcribe — Transcribe images and return result URLs/upload-files skill, then continue to step 2.Call htr_transcribe once with ALL image URLs in a single call.
Batching rule: Never call htr_transcribe multiple times for separate
images. Each call runs an expensive GPU pipeline — batch everything.
After transcription, present results as an inline artifact for the viewer and downloadable links for data exports.
Download the viewer HTML, then inline all external dependencies (OpenSeadragon JS and images) so the artifact is fully self-contained (the artifact sandbox blocks external requests).
curl -sL "{viewer_url}" -o /home/claude/viewer.html
Then run this Python script to embed dependencies:
import re, base64, urllib.request
with open("/home/claude/viewer.html", "r") as f:
html = f.read()
# Inline OpenSeadragon JS (CDN script -> inline script)
osd_match = re.search(r'<script src="(https://cdn[^"]+openseadragon[^"]+)">\s*</script>', html)
if osd_match:
with urllib.request.urlopen(osd_match.group(1)) as resp:
osd_js = resp.read().decode()
html = html.replace(osd_match.group(0), f"<script>{osd_js}</script>")
# Embed all Gradio image URLs as base64 data URIs
for url in set(re.findall(
r'https://riksarkivet-htr-demo\.hf\.space/gradio_api/file=[^\s"]+\.(?:jpg|png)', html
)):
with urllib.request.urlopen(url) as resp:
img_data = resp.read()
ext = "jpeg" if url.endswith(".jpg") else "png"
data_uri = f"data:image/{ext};base64,{base64.b64encode(img_data).decode()}"
html = html.replace(url, data_uri)
with open("/mnt/user-data/outputs/viewer.html", "w") as f:
f.write(html)
Then call present_files with /mnt/user-data/outputs/viewer.html to render
the interactive viewer as an inline artifact.
Provide the remaining URLs as clickable download links:
- Transcription data: [pages_url] (per-line JSON)
- Export: [export_url] (archival export)
Do NOT reproduce document text as plain text in your response — present the artifact and links instead.
| Value | Use when |
|---|---|
swedish | Swedish handwriting (default) |
norwegian | Norwegian handwriting |
english | English handwriting |
medieval | Medieval scripts |
| Value | Use when |
|---|---|
single_page | Single pages, snippets, cropped regions (default) |
spread | Two-page book openings (Swedish only) |
| Value | Description |
|---|---|
alto_xml | ALTO XML — standard archival (default) |
page_xml | PAGE XML — alternative archival format |
json | JSON — structured data format |
custom_yaml accepts a raw HTRflow YAML config string. Overrides
language and layout. Use only when user explicitly provides one.
Example — English modern handwriting with a custom TrOCR model:
steps:
- step: Segmentation
settings:
model: yolo
model_settings:
model: Riksarkivet/yolov9-lines-within-regions-1
- step: TextRecognition
settings:
model: TrOCR
model_settings:
model: microsoft/trocr-base-handwritten
generation_settings:
batch_size: 16
- step: OrderLines