Verified, byte-for-byte mirror of the official pretrained crnn_vgg16_bn recognition checkpoint distributed by docTR. The rrainn organization maintains this mirror for reproducible, immutable model resolution through the Hugging Face Hub.
This is a redistribution of an upstream docTR artifact. rrainn did not train or modify the model and is not affiliated with or endorsed by Mindee or the docTR maintainers.
Model details
Property
Value
Task
Cropped-word text recognition
Architecture
CRNN with a VGG16-BN feature extractor, bidirectional LSTM decoder, and CTC decoding
Framework
PyTorch via python-doctr
Input
RGB word crop, normalized and resized for a 3 × 32 × 128 model input
Output
Recognized text and sequence confidence
Vocabulary
docTR french vocabulary: digits, ASCII Latin letters, punctuation, common currency symbols, and French accented characters
Maximum sequence length
32 characters
Upstream model version
Checkpoint introduced for docTR v0.12.0; mirrored from the configuration shipped in docTR v1.0.1
This model is intended for recognizing text in already-localized word images as part of an OCR pipeline. Suitable uses include document transcription, OCR benchmarking, and initialization for domain-specific fine-tuning.
This checkpoint is not a full-page detector. Pair it with a text detection model through docTR's ocr_predictor, or provide appropriately cropped word images through recognition_predictor.
Usage
Install the upstream runtime:
pip install "python-doctr==1.0.1"
Load directly with docTR's native Hugging Face integration:
python
1from doctr.models import from_hub, recognition_predictor
23# Pin a repository commit in production for reproducible model resolution.4model = from_hub("rrainn/doctr-crnn-vgg16-bn", revision="48f1ff1b42ae89ae5c28811d21d8b968aafcb436")5predictor = recognition_predictor(model)
For integrity-sensitive deployments, verify pytorch_model.bin against SHA256SUMS before loading the checkpoint.
Preprocessing
The docTR configuration bundled with this repository defines:
RGB channel order
Input shape: 3 × 32 × 128
Mean: (0.694, 0.695, 0.693)
Standard deviation: (0.299, 0.296, 0.301)
Use docTR's recognition predictor to apply the matching preprocessing and decoding behavior.
Training data and evaluation
The upstream checkpoint is described by docTR as pretrained on its text-recognition dataset. The exact training dataset composition, dataset versions, filtering, and training hyperparameters are not disclosed alongside this artifact. Accordingly, this mirror does not declare Hugging Face datasets metadata or make unsupported claims about training provenance.
rrainn has not independently benchmarked this mirrored checkpoint. docTR publishes recognition-model evaluations on FUNSD and CORD in its model-selection documentation; consult the upstream documentation for its methodology and current results.
Limitations and risks
Recognition is limited to the configured Latin/French character vocabulary. Other scripts and unsupported characters cannot be represented.
Accuracy may degrade on handwriting, curved or vertical text, severe blur, unusual typography, low contrast, or domains unlike the undisclosed training distribution.
Because the complete training-data composition is unavailable, demographic, geographic, linguistic, and source-domain biases cannot be fully assessed.
Sequence confidence is not guaranteed to be calibrated and should not be interpreted as a probability of correctness without task-specific validation.
OCR output can contain consequential errors. Human review and domain-specific evaluation are required for legal, medical, financial, identity, accessibility, or other high-impact uses.
Security
PyTorch checkpoint formats should be treated as executable-adjacent untrusted input. Download only from a trusted revision, verify the published SHA-256 digest, and use a current PyTorch release with restricted loading such as torch.load(..., weights_only=True) where applicable. This mirror preserves the exact upstream bytes and does not add serialized code.
Artifact provenance
The binary was downloaded from the URL embedded in docTR v1.0.1 and renamed from crnn_vgg16_bn-0417f351.pt to the docTR Hub-standard pytorch_model.bin. Its bytes were not changed. Machine-readable provenance is available in PROVENANCE.json; the digest is also recorded in SHA256SUMS.
License and attribution
The upstream docTR project distributes this work under the Apache License 2.0. This repository includes the complete license text and an attribution NOTICE. The checkpoint is redistributed without modification and without warranty.
The Apache-2.0 license does not grant trademark rights. Names such as docTR and Mindee are used only to identify the origin and architecture of the artifact. Users are responsible for confirming that their use of the model and any underlying data complies with applicable laws and third-party rights.
Citation
bibtex
1@inproceedings{shi2016crnn,
2 title={An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition},
3 author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
4 booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
5 year={2016}
6}
Please also cite the docTR project when this implementation or checkpoint materially contributes to your work.
