Create app.py

app.py

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+import gradio as gr
+import cv2
+import pytesseract
+from pytesseract import Output
+import numpy as np
+
+def text_detection(img, config="--psm 11 --oem 3"):
+    data = pytesseract.image_to_data(img, config=config, output_type=Output.DICT)
+    horizontal_text = []
+    vertical_text = []
+
+    for i in range(len(data['text'])):
+        if int(data['conf'][i]) > 20:
+            x, y, w, h = data['left'][i], data['top'][i], data['width'][i], data['height'][i]
+            text = data['text'][i]
+            if w > h:
+                horizontal_text.append(text)
+            else:
+                vertical_text.append(text)
+    return horizontal_text, vertical_text, data
+
+def draw_boxes(img, data):
+    for i in range(len(data['text'])):
+        if int(data['conf'][i]) > 20:
+            x, y, w, h = data['left'][i], data['top'][i], data['width'][i], data['height'][i]
+            text = data['text'][i]
+            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
+            cv2.putText(img, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+    return img
+
+def word_level_accuracy(data, ground_truth):
+    ocr_text = ' '.join([text for text in data['text'] if text.strip()])
+    gt_words = set(ground_truth.split())
+    ocr_words = set(ocr_text.split())
+    correct = gt_words.intersection(ocr_words)
+    return (len(correct) / len(gt_words)) * 100 if gt_words else 0
+
+def character_level_accuracy(data, ground_truth):
+    ocr_text = ''.join([text.strip() for text in data['text']])
+    gt_chars = set(ground_truth.replace(" ", ""))
+    ocr_chars = set(ocr_text.replace(" ", ""))
+    correct = gt_chars.intersection(ocr_chars)
+    return (len(correct) / len(gt_chars)) * 100 if gt_chars else 0
+
+def process(image, ground_truth):
+    if image is None:
+        return None, "Please upload an image."
+
+    img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    h_text, v_text, data = text_detection(img_bgr)
+    word_acc = word_level_accuracy(data, ground_truth)
+    char_acc = character_level_accuracy(data, ground_truth)
+
+    result_img = draw_boxes(img_bgr.copy(), data)
+    result_img_rgb = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
+
+    results = f"**Horizontal Text**: {' '.join(h_text) if h_text else 'None'}\n\n"
+    results += f"**Vertical Text**: {' '.join(v_text) if v_text else 'None'}\n\n"
+    results += f"**Word-Level Accuracy**: {word_acc:.2f}%\n"
+    results += f"**Character-Level Accuracy**: {char_acc:.2f}%"
+
+    return result_img_rgb, results
+
+demo = gr.Interface(
+    fn=process,
+    inputs=[
+        gr.Image(type="numpy", label="Upload Image"),
+        gr.Textbox(lines=4, placeholder="Enter ground truth text here", label="Ground Truth")
+    ],
+    outputs=[
+        gr.Image(type="numpy", label="Detected Text with Bounding Boxes"),
+        gr.Markdown()
+    ],
+    title="OCR Accuracy Evaluator with Bounding Boxes",
+    description="Upload an image and ground truth text to evaluate Tesseract OCR accuracy by word and character. Bounding boxes are drawn around detected text."
+)
+
+if __name__ == "__main__":
+    demo.launch()