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import PIL | |
from PIL import ImageDraw | |
from PIL import Image | |
import streamlit as st | |
import os | |
def load_image(image_file): | |
img = PIL.Image.open(image_file) | |
return img | |
def init_session_states(): | |
if 'disp' not in st.session_state: | |
st.session_state['disp'] = st.empty() | |
st.session_state['disp'].text("Setting up environment with latest build of easyocr. This will take about a minute ") | |
if 'init' not in st.session_state: | |
st.session_state['init'] = 1 | |
os.system('pip install git+git://github.com/jaidedai/easyocr.git') | |
os.system('pip install git+https://github.com/huggingface/transformers.git --upgrade') | |
init_session_states() | |
import easyocr | |
from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
def text_recognition(image): | |
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") | |
#processor = TrOCRProcessor.from_pretrained("microsoft/trocr-large-handwritten") | |
#model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-large-handwritten") | |
pixel_values = processor(image, return_tensors="pt").pixel_values | |
generated_ids = model.generate(pixel_values) | |
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
st.write(generated_text) | |
def main(): | |
st.session_state['disp'].text("Env setup up Complete") | |
uploaded_file = st.file_uploader("Choose image file to detect text",type=['jpeg','jpg']) | |
if uploaded_file is not None: | |
file_details = {"FileName":uploaded_file.name,"FileType":uploaded_file.type,"FileSize":uploaded_file.size} | |
st.write(file_details) | |
image = load_image(uploaded_file) | |
st.image(image,width=500) | |
st.write("Detecting text bounding box and Take 1 recognition...") | |
reader = easyocr.Reader(['en'],gpu=True) | |
bound = reader.readtext(image) | |
st.write("Bounding box Detection complete") | |
st.write(str(bound)) | |
st.write("Recognizing text - Take 2....") | |
text_recognition(image) | |
if __name__ == "__main__": | |
main() | |