Image-to-Text
Transformers
PyTorch
TensorBoard
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Russian
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vision-encoder-decoder
image-text-to-text
ocr
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("raxtemur/trocr-base-ru")
model = AutoModelForMultimodalLM.from_pretrained("raxtemur/trocr-base-ru")Quick Links
Model Card for TrOCR-Ru
Finetuned model microsoft/trocr-base-handwritten on large synth datasets from nastyboget.
Metrics on HKR/Cyrillic datasets
| Metric | HKR_val | HKR_test1 | HKR_test2 | CYR_val | CYR_test |
|---|---|---|---|---|---|
| Accuracy | 69.9947 | 67.4184 | 69.9187 | 72.3613 | 63.9249 |
| CER | 6.7964 | 8.9113 | 6.7278 | 6.6403 | 9.2576 |
| WER | 21.6688 | 27.3849 | 21.6200 | 27.6715 | 33.2406 |
Last update form 29/02/2024
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# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="raxtemur/trocr-base-ru")