Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Document OCR using dots.ocr

This dataset contains OCR results from images in NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset using DoTS.ocr, a compact 1.7B multilingual model.

Processing Details

Source Dataset: NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset
Model: rednote-hilab/dots.ocr
Number of Samples: 10
Processing Time: 2.2 min
Processing Date: 2025-10-08 16:53 UTC

Configuration

Image Column: image
Output Column: markdown
Dataset Split: train
Batch Size: 32
Prompt Mode: layout-all
Max Model Length: 32,768 tokens
Max Output Tokens: 8,192
GPU Memory Utilization: 80.0%

Model Information

DoTS.ocr is a compact multilingual document parsing model that excels at:

🌍 100+ Languages - Multilingual document support
📊 Table extraction - Structured data recognition
📐 Formulas - Mathematical notation preservation
📝 Layout-aware - Reading order and structure preservation
🎯 Compact - Only 1.7B parameters

Dataset Structure

The dataset contains all original columns plus:

markdown: The extracted text in markdown format
inference_info: JSON list tracking all OCR models applied to this dataset

Usage

from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("{output_dataset_id}", split="train")

# Access the markdown text
for example in dataset:
    print(example["markdown"])
    break

# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
    print(f"Column: {info['column_name']} - Model: {info['model_id']}")

Reproduction

This dataset was generated using the uv-scripts/ocr DoTS OCR script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
    NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \
    <output-dataset> \
    --image-column image \
    --batch-size 32 \
    --prompt-mode layout-all \
    --max-model-len 32768 \
    --max-tokens 8192 \
    --gpu-memory-utilization 0.8

Generated with 🤖 UV Scripts

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