metadata
language:
- fa
library_name: hezar
tags:
- image-to-text
- hezar
pipeline_tag: image-to-text
A CRNN model for Persian OCR. This model is based on a simple CNN + LSTM architecture inspired by this paper. More info about data and training will be provided soon.
Note that this model is only optimized for scanned documents and supports input characters of up to 32 (For an end-to-end OCR pipeline, use a text detector model first 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.
Limitations
This model is best suited for Persian alphabet and lacks the ability to recognize numbers and digits properly. We'll soon retrain this model to fit all scenarios.
Usage
pip install hezar
from hezar.models import Model
crnn = Model.load("hezarai/crnn-base-fa-64x256")
texts = crnn.predict(["sample_image.jpg"])
print(texts)