Increase max output length from 64 to 96 (Max length of the samples in the dataset was 48 to handle CTC loss issues)
Support numbers and special characters (see id2label in
model_config.yaml
)
Auto-handling of LTR characters like digits in between the text
Note that this model is only optimized for printed/scanned documents and works best on texts with a length of up to 50-ish characters. (For an end-to-end OCR pipeline, use a text detector model first like
https://huggingface.co/hezarai/CRAFT
to
extract text boxes preferrably in word-level and then use this model), but it can be used to be fine-tuned on other domains like license plate or handwritten texts.
Usage
pip install hezar
from hezar.models import Model
crnn = Model.load("hezarai/crnn-base-fa-v2")
texts = crnn.predict(["sample_image.jpg"])
print(texts)
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More Information About crnn-base-fa-v2 huggingface.co Model
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crnn-base-fa-v2 is an open source model from GitHub that offers a free installation service, and any user can find crnn-base-fa-v2 on GitHub to install. At the same time, huggingface.co provides the effect of crnn-base-fa-v2 install, users can directly use crnn-base-fa-v2 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.