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"): entity = str(entity) entities.append(entity) entities = "\n".join(entities) print("ENTITIES ", entities) return entities # If you don't have tesseract executable in your PATH, include the following: # pytesseract.pytesseract.tesseract_cmd = r'' # 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") # with gr.Column(): # gr.CheckboxGroup(ner, label="Named entities") btn.click(fn=run, inputs=[image_in, lang], outputs=[text_out, ner]) demo.launch()