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| import pytesseract | |
| from PIL import Image | |
| import pandas as pd | |
| import os | |
| import gradio as gr | |
| import numpy as np | |
| import gradio as gr | |
| def ocr_df_using_pytesseract(image): | |
| #pytesseract.pytesseract.tesseract_cmd =r"C:\Users\amold\Desktop\Upwork\pdf to image and pytesseract\tesseact_exe\Tesseract-OCR\tesseract.exe" | |
| pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract' | |
| #image = Image.open(example['image_path']) | |
| width, height = image.size | |
| # apply ocr to the image | |
| ocr_df = pytesseract.image_to_data(image, output_type='data.frame') | |
| float_cols = ocr_df.select_dtypes('float').columns | |
| ocr_df = ocr_df.dropna().reset_index(drop=True) | |
| ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int) | |
| ocr_df = ocr_df.replace(r'^\s*$', np.nan, regex=True) | |
| ocr_df = ocr_df.dropna().reset_index(drop=True) | |
| ocr_df | |
| ocr_df['X1']=ocr_df['left'] | |
| ocr_df['Y1']=ocr_df['top'] | |
| ocr_df['X2']= ocr_df['left'] + ocr_df['width'] | |
| ocr_df['Y2']= ocr_df['top'] + ocr_df['height'] | |
| return ocr_df | |
| def image_to_text(image): | |
| ocr_df= ocr_df_using_pytesseract(image) | |
| grouped_text = ocr_df.groupby(['block_num', 'line_num'])['text'].agg(' '.join).reset_index() | |
| # sort the text by line numbers within each block | |
| grouped_text = grouped_text.sort_values(['block_num', 'line_num']) | |
| # join the text by blocks and add newlines | |
| result = '' | |
| for i, row in grouped_text.iterrows(): | |
| if i > 0 and row['block_num'] != grouped_text.loc[i-1, 'block_num']: | |
| result += '\n\n' | |
| result += row['text'].rstrip() + '\n' | |
| return result | |
| demo = gr.Interface(fn=image_to_text, | |
| inputs= gr.Image(type="pil"), | |
| outputs=["text"], | |
| title="Menu image to text", | |
| description= "Upload Menu image here") | |
| demo.launch(share=False) |