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from PIL import Image
import pytesseract
import gradio as gr
import os
from flair.data import Sentence
from flair.models import SequenceTagger
from segtok.segmenter import split_single
tagger = SequenceTagger.load("ner-ontonotes")
langs = []
choices = os.popen("tesseract --list-langs").read().split("\n")[1:-1]
blocks = gr.Blocks()
def get_named_entities(ocr_text: str):
sentence = [Sentence(sent, use_tokenizer=True) for sent in split_single(ocr_text)]
tagger.predict(sentence)
entities = []
for token in sentence:
for entity in token.get_spans("ner"):
entities.append(entity)
print("ENTITIES ", entities)
entities = "/n".join(entities)
return entities
# If you don't have tesseract executable in your PATH, include the following:
# pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'
# Example tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract'
# Simple image to string
# print(pytesseract.image_to_string(Image.open('eurotext.png')))
# # French text image to string
# print(pytesseract.image_to_string(Image.open('test-european.jpg'), lang='fra'))
# # Get bounding box estimates
# print(pytesseract.image_to_boxes(Image.open('test.png')))
# # Get verbose data including boxes, confidences, line and page numbers
# print(pytesseract.image_to_data(Image.open('test.png')))
# # Get information about orientation and script detection
# print(pytesseract.image_to_osd(Image.open('test.png'))
def run(image, lang="eng"):
result = pytesseract.image_to_string(image, lang=None if lang == [] else lang)
ner = get_named_entities(result)
return result, ner
with gr.Blocks() as demo:
gr.Markdown("## Theatre Programmer")
with gr.Row():
with gr.Column():
image_in = gr.Image(type="pil")
lang = gr.Dropdown(choices, value="eng")
btn = gr.Button("Run")
with gr.Column():
text_out = gr.TextArea(label="OCR output")
with gr.Column():
ner = gr.TextArea(label="Named entities")
btn.click(fn=run, inputs=[image_in, lang], outputs=[text_out, ner])
demo.launch()
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