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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) | |
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() | |
with gr.Column(): | |
ner = gr.TextArea() | |
btn.click(fn=run, inputs=[image_in, lang], outputs=[text_out, ner]) | |
demo.launch() | |