test

app.py

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+import os
+import subprocess
+
+# Ensure poppler-utils and tesseract-ocr are installed
+def install_dependencies():
+    try:
+        subprocess.run(["apt-get", "update"], check=True)
+        subprocess.run(["apt-get", "install", "-y", "poppler-utils", "tesseract-ocr", "libtesseract-dev"], check=True)
+        subprocess.run(["mkdir", "-p", "/usr/share/tesseract-ocr/4.00/tessdata"], check=True)
+        subprocess.run(["wget", "https://github.com/tesseract-ocr/tessdata_best/raw/main/tel.traineddata", "-P", "/usr/share/tesseract-ocr/4.00/tessdata/"], check=True)
+    except subprocess.CalledProcessError as e:
+        print(f"An error occurred while installing dependencies: {e}")
+        raise
+
+install_dependencies()
+
+os.environ["TESSDATA_PREFIX"] = "/usr/share/tesseract-ocr/4.00/"
+
+import cv2 as cv
+import numpy as np
+import pytesseract
+from pdf2image import convert_from_path
+import gradio as gr
+import json
+import os
+
+# Function to rescale the frame
+def rescaleFrame(frame, scale=0.75):
+    width = int(frame.shape[1] * scale)
+    height = int(frame.shape[0] * scale)
+    dimensions = (width, height)
+    return cv.resize(frame, dimensions, interpolation=cv.INTER_AREA)
+
+# Function to apply gamma correction
+def apply_gamma(image, gamma=1.0):
+    invGamma = 1.0 / gamma
+    table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
+    return cv.LUT(image, table)
+
+# Function to apply adaptive thresholding
+def adaptive_threshold(image):
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    return cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 11, 2)
+
+# Function to apply edge detection
+def edge_detection(image):
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    return cv.Canny(gray, 50, 150)
+
+# Function to apply morphological transformations
+def morphological_transformation(image):
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+    _, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+    kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
+    return cv.morphologyEx(binary, cv.MORPH_CLOSE, kernel)
+
+# Function to process image for text extraction
+def process_image(img, method='default'):
+    resized_image = rescaleFrame(img)
+
+    if method == 'default':
+        gray = cv.cvtColor(resized_image, cv.COLOR_BGR2GRAY)
+        blur = cv.GaussianBlur(gray, (3, 3), 0)
+        gamma_corrected = apply_gamma(blur, gamma=0.3)
+        _, thresh = cv.threshold(gamma_corrected, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
+        kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
+        return cv.morphologyEx(thresh, cv.MORPH_CLOSE, kernel)
+    elif method == 'adaptive_threshold':
+        return adaptive_threshold(resized_image)
+    elif method == 'edge_detection':
+        return edge_detection(resized_image)
+    elif method == 'morphological':
+        return morphological_transformation(resized_image)
+
+# Function to extract text from processed image
+def extract_text_from_image(image, langs='tel'):
+    return pytesseract.image_to_string(image, lang=langs)
+
+output_dir = "output"
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+all_texts = {}
+
+def save_and_next(page_num, text, extracted_texts, original_images, total_pages):
+    page_num = int(page_num)  # Ensure page_num is an integer
+    total_pages = int(total_pages)  # Ensure total_pages is an integer
+    formatted_text = {
+        f"Page number: {page_num}": {
+            "Content": [
+                line for line in text.split('\n') if line.strip() != ''
+            ]
+        }
+    }
+    all_texts.update(formatted_text)
+    json_path = os.path.join(output_dir, "all_texts.json")
+    with open(json_path, 'w', encoding='utf-8') as f:
+        json.dump(all_texts, f, ensure_ascii=False, indent=4)
+
+    next_page_num = page_num + 1  # Increment to next page
+    if next_page_num <= total_pages:
+        next_page_image = original_images[next_page_num - 1]
+        methods = ['default', 'adaptive_threshold', 'edge_detection', 'morphological']
+        best_text = ""
+        max_confidence = -1
+        for method in methods:
+            processed_image = process_image(next_page_image, method=method)
+            text = extract_text_from_image(processed_image, langs='tel')
+            confidence = len(text)
+            if confidence > max_confidence:
+                max_confidence = confidence
+                best_text = text
+        extracted_texts.append(best_text)
+        return gr.update(value=best_text), next_page_num, gr.update(value=next_page_image, height=None, width=None), json_path
+    else:
+        return "All pages processed", page_num, None, json_path
+
+def skip_page(page_num, extracted_texts, original_images, total_pages):
+    next_page_num = int(page_num) + 1  # Ensure page_num is an integer and increment to next page
+    total_pages = int(total_pages)  # Ensure total_pages is an integer
+    if next_page_num <= total_pages:
+        next_page_image = original_images[next_page_num - 1]
+        methods = ['default', 'adaptive_threshold', 'edge_detection', 'morphological']
+        best_text = ""
+        max_confidence = -1
+        for method in methods:
+            processed_image = process_image(next_page_image, method=method)
+            text = extract_text_from_image(processed_image, langs='tel')
+            confidence = len(text)
+            if confidence > max_confidence:
+                max_confidence = confidence
+                best_text = text
+        extracted_texts.append(best_text)
+        return gr.update(value=best_text), next_page_num, gr.update(value=next_page_image, height=None, width=None)
+    else:
+        return "All pages processed", page_num, None
+
+def upload_pdf(pdf):
+    pdf_path = pdf.name
+    pages = convert_from_path(pdf_path)
+    first_page = np.array(pages[0])
+    methods = ['default', 'adaptive_threshold', 'edge_detection', 'morphological']
+    best_text = ""
+    max_confidence = -1
+    for method in methods:
+        processed_image = process_image(first_page, method=method)
+        text = extract_text_from_image(processed_image, langs='tel')
+        confidence = len(text)
+        if confidence > max_confidence:
+            max_confidence = confidence
+            best_text = text
+    original_images = [np.array(page) for page in pages]
+    extracted_texts = [best_text]
+    return gr.update(value=original_images[0], height=None, width=None), gr.update(value=best_text), 1, extracted_texts, original_images, len(pages)
+
+def navigate_to_page(page_num, extracted_texts, original_images):
+    if 0 <= page_num - 1 < len(original_images):
+        return gr.update(value=original_images[page_num - 1], height=None, width=None), gr.update(value=extracted_texts[page_num - 1]), page_num
+    else:
+        return gr.update(value="Invalid Page Number"), None, page_num
+
+def display_pdf_and_text():
+    with gr.Blocks() as demo:
+        gr.Markdown("## PDF Viewer and Text Editor")
+        pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
+        with gr.Row():
+            image_output = gr.Image(label="Page Image", type="numpy")
+            text_editor = gr.Textbox(label="Extracted Text", lines=10, interactive=True)
+        page_num = gr.Number(value=1, label="Page Number", visible=True)
+        extracted_texts = gr.State()
+        original_images = gr.State()
+        total_pages = gr.State()
+        save_next_button = gr.Button("Save and Next")
+        skip_button = gr.Button("Skip")
+        pdf_input.upload(upload_pdf, inputs=pdf_input, outputs=[image_output, text_editor, page_num, extracted_texts, original_images, total_pages])
+
+        save_next_button.click(fn=save_and_next,
+                               inputs=[page_num, text_editor, extracted_texts, original_images, total_pages],
+                               outputs=[text_editor, page_num, image_output, gr.File(label="Download JSON")])
+
+        skip_button.click(fn=skip_page,
+                          inputs=[page_num, extracted_texts, original_images, total_pages],
+                          outputs=[text_editor, page_num, image_output])
+
+        page_buttons = gr.Row()
+
+        def update_page_buttons(total_pages, extracted_texts, original_images):
+            page_buttons.clear()  # Clear previous buttons if any
+            buttons = []
+            for i in range(1, total_pages + 1):
+                button = gr.Button(str(i), variant="primary", size="small")
+                button.click(navigate_to_page, inputs=[i, extracted_texts, original_images], outputs=[image_output, text_editor, page_num])
+                buttons.append(button)
+            return buttons
+
+        total_pages.change(fn=update_page_buttons, inputs=[total_pages, extracted_texts, original_images], outputs=[page_buttons])
+
+    return demo
+
+iface = display_pdf_and_text()
+iface.launch()

packages.txt

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+poppler-utils
+tesseract-ocr
+libtesseract-dev

requirements.txt

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+opencv-python==4.10.0.82
+opencv-python-headless==4.10.0.82
+numpy==1.26.4
+pytesseract==0.3.10
+pdf2image==1.17.0
+gradio==4.36.1
+gradio_client==1.0.1
+json5==0.9.25
+jsonpointer==2.4
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+fastjsonschema==2.19.1
+orjson==3.10.4
+python-json-logger==2.0.7
+ujson==5.10.0
+!./setup.sh

setup.sh

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+#!/bin/bash
+apt-get update
+apt-get install -y poppler-utils tesseract-ocr libtesseract-dev
+mkdir -p /usr/share/tesseract-ocr/4.00/tessdata
+wget https://github.com/tesseract-ocr/tessdata_best/raw/main/tel.traineddata -P /usr/share/tesseract-ocr/4.00/tessdata/