GOCR — KSVTRv3-de (deutscher Recognizer), ONNX/CPU · Apache-2.0 (built on OpenOCR + PaddleOCR)

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NOTICE

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+GOCR (g-ocr)
+Copyright (c) 2026 Keyvan Hardani / german-ocr.de
+
+Licensed under the Apache License, Version 2.0 (see LICENSE).
+
+Third-party software and models
+-------------------------------
+This product is built on the following Apache-2.0 works; their copyright
+and license notices are retained:
+
+* OpenOCR  -  https://github.com/Topdu/OpenOCR
+  Apache License 2.0. GOCR's detector (DB-based) and recognizer
+  (RepSVTR / SVTR-family, "KSVTRv3") are built on OpenOCR models and
+  its training framework.
+
+* PaddleOCR  -  https://github.com/PaddlePaddle/PaddleOCR
+  Apache License 2.0. The recognizer character dictionary
+  (ppocr_keys_v1.txt) originates from PaddleOCR.
+
+Modifications by GOCR: ONNX export and packaging. A German-specialized
+recognizer (KSVTRv3-de), trained on German corpora + domain data, is in
+progress and will be published separately. The currently published
+recognizer uses the multilingual base vocabulary (ppocr_keys_v1).

README.md

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+---
+license: apache-2.0
+language:
+- de
+tags:
+- ocr
+- german
+- document-ai
+- layout
+- onnx
+- cpu
+- bbox
+- vision
+pipeline_tag: image-to-text
+library_name: g-ocr
+---
+
+# GOCR — schnelle, kleine deutsche OCR-/Vision-Schicht (CPU)
+
+GOCR liest ein **ganzes Dokument** zu **Text + Position (bbox)** als strukturiertes JSON —
+**schnell, ~30 MB, reine CPU (kein GPU)**.
+
+Gedacht als **Vision-/OCR-Schicht für (text-only) LLM-Pipelines** und als **Tooling**:
+präzise Layout-Boxen + Text rein → dein LLM macht Verständnis/Extraktion.
+
+```bash
+pip install g-ocr           # Bilder: png/jpg/webp/tiff/bmp ...
+pip install "g-ocr[pdf]"    # + PDF (optionales Plugin)
+```
+```python
+import g_ocr
+ocr = g_ocr.from_pretrained()
+res = ocr.read("dokument.png")            # ein Bild      -> {text, regions:[{text, box, quad, score}]}
+doc = ocr.read_document("rechnung.pdf")   # PDF/mehrseitig -> {n_pages, pages:[...], text}
+```
+🔗 Demo: **[huggingface.co/spaces/Keyven/GOCR-Demo](https://huggingface.co/spaces/Keyven/GOCR-Demo)** · 🌐 **[german-ocr.de](https://german-ocr.de)**
+
+## Wofür GOCR stark ist
+- 🎯 **Präzise Bounding-Boxes + ganzes Dokument** (Lesereihenfolge) → strukturiertes JSON
+- ⚡ **CPU, bis ~16× schneller als EasyOCR** — kein GPU
+- 📦 **~30 MB** (Detektor + Recognizer, ONNX)
+- 🧱 **Fraktur-robust** (3× genauer als EasyOCR, s. u.) · on-prem / DSGVO-freundlich
+- 🤖 **LLM-ready**: sauberes JSON (`text` + `box` + `quad`) als Input für text-only LLMs
+- 🗂️ **Bilder + PDF**: png/jpg/webp/tiff/bmp … und PDF (bis ~500 Seiten) — ein API-Aufruf, JSON je Seite
+
+## Benchmarks (deutsche Eval-Sets, CPU)
+
+**KSVTRv3-de** — deutscher Recognizer, eigene deutsche Eval-Sets (NED ↑ = Zeichen-Ähnlichkeit, höher = besser):
+
+| Set | NED ↑ | ~CER |
+|---|--:|--:|
+| Modernes Deutsch (clean) | **0,91** | ~9 % |
+| Degradierte Scans (Augraphy) | **0,85** | ~15 % |
+| Fraktur (NewsEye, real) | **0,74** | ~26 % |
+
+**Einordnung:** KSVTRv3-de ist ein **deutscher Spezialist** — robust auf echten/verrauschten Scans und Fraktur (Realismus-Training mit Augraphy). Trainiert auf deutschen Korpora (Leipzig) + Domänenfeldern (Rechnung/IBAN/USt-IdNr) + 2642 Dokument-Fonts. Auf sauberem modernem Deutsch sind dedizierte Engines (z. B. Tesseract) bei reiner Zeichengenauigkeit teils vorn; GOCRs Stärke ist die **robuste, on-prem, integrierte** Dokument→JSON-Schicht (CPU, klein, LLM-ready).
+
+## JSON-Schema
+```json
+{
+  "engine": "GOCR", "version": "0.2.0",
+  "image": {"width": 1414, "height": 2000},
+  "text": "…Volltext in Lesereihenfolge…",
+  "n_regions": 50,
+  "regions": [
+    {"id": 0, "text": "Rechnungsnummer", "score": 0.99,
+     "box": [217, 723, 465, 752], "quad": [[217,723],[465,723],[465,752],[217,752]]}
+  ]
+}
+```
+
+## Nutzung
+```bash
+g-ocr dokument.png              # JSON (Text + box + quad)
+g-ocr dokument.png --text-only  # nur Text (Lesereihenfolge)
+```
+
+## Architektur
+Zwei Stufen, reines ONNX/CPU: **GOCR-Detektor** (DB-basiert) + **KSVTRv3-de-Recognizer** (SVTR-Encoder + CTC, deutscher Charset).
+
+![GOCR Architektur](assets/gocr_architecture_v2.png)
+
+So entsteht der deutsche Recognizer (Daten-Foundation → Training → Deploy):
+
+![GOCR Training-Pipeline](assets/gocr_training_pipeline_v2.png)
+
+## Limitationen
+- Fokus ist die **Layout-+-Text-Schicht** für Pipelines, nicht der Spitzenwert bei reiner Modern-DE-Texterkennung.
+- **Wort-Spacing**: Wortgrenzen sind nicht immer korrekt (schwächeres Bag-of-Words) — Zeichengenauigkeit (CER) ist davon kaum betroffen.
+- Sehr kleine/verrauschte Schrift + extreme Layouts können Detektion/Erkennung erschweren.
+- Reine OCR — kein Sprachverständnis (das macht das nachgelagerte LLM).
+
+## Credits & Upstream
+GOCR baut auf hervorragender Open-Source-Arbeit auf (jeweils **Apache-2.0**):
+- **OpenOCR** ([Topdu/OpenOCR](https://github.com/Topdu/OpenOCR)) — Detektor (DB) + Recognizer (RepSVTR / SVTR-Familie) und Trainings-Framework.
+- **PaddleOCR** ([PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) — Zeichen-Dictionary (`ppocr_keys_v1`).
+
+Beide stehen unter Apache-2.0; die Lizenz- und Urheberhinweise gelten fort (siehe `NOTICE`).
+
+> **Aktuelles Modell:** Der Recognizer ist **KSVTRv3-de** (deutscher Charset, 144 Zeichen), via Transfer-Learning vom OpenOCR-Encoder auf deutschen Korpora + Domänendaten + Scan-Realismus (Augraphy) trainiert.
+
+## Lizenz
+**Apache-2.0** — siehe `LICENSE` und `NOTICE`.
+
+---
+**Das KI-Büro für Ihre Dokumente** → **[german-ocr.de](https://german-ocr.de)**

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