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@@ -37,3 +37,4 @@ en_PP-OCRv3_det_infer/inference.pdiparams filter=lfs diff=lfs merge=lfs -text
 en_PP-OCRv3_det_infer/inference.pdmodel filter=lfs diff=lfs merge=lfs -text
 en_PP-OCRv3_rec_infer/inference.pdiparams filter=lfs diff=lfs merge=lfs -text
 en_PP-OCRv3_rec_infer/inference.pdmodel filter=lfs diff=lfs merge=lfs -text
+tesseract-ocr-w64-setup-5.3.3.20231005.exe filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,39 +1,53 @@
-from paddleocr import PaddleOCR
-import requests
 import numpy as np
-from PIL import Image
-from io import BytesIO
 import json
 import gradio as gr
-import paddleocr
+import easyocr
+
+reader = easyocr.Reader(['en'])
 
 # ocr = PaddleOCR(use_angle_cls=True, lang='en', use_pdserving=False, cls_batch_num=8, det_batch_num=8, rec_batch_num=8)
 
-ocr = PaddleOCR(use_angle_cls=True, lang='en')
+# ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
-def index(url):
-    response = requests.get(url)
-    img = Image.open(BytesIO(response.content))
-    resize_factor = 1
-    new_size = tuple(int(dim * resize_factor) for dim in img.size)
-    img = img.resize(new_size, Image.Resampling.LANCZOS)
+# def index(url):
+#     response = requests.get(url)
+#     img = Image.open(BytesIO(response.content))
+#     resize_factor = 1
+#     new_size = tuple(int(dim * resize_factor) for dim in img.size)
+#     img = img.resize(new_size, Image.Resampling.LANCZOS)
 
-    img_array = np.array(img.convert('RGB'))
+#     img_array = np.array(img.convert('RGB'))
 
-    result = ocr.ocr(img_array)
+#     result = ocr.ocr(img_array)
 
-    boxes = [line[0] for line in result]
-    txts = [line[1][0] for line in result]
-    scores = [line[1][1] for line in result]
+#     boxes = [line[0] for line in result]
+#     txts = [line[1][0] for line in result]
+#     scores = [line[1][1] for line in result]
 
-    print(boxes)
-    print(txts)
+#     print(boxes)
+#     print(txts)
 
-    output_dict = {"texts": txts, "boxes": boxes, "scores": scores}
-    output_json = json.dumps(output_dict)  # Convert to JSON string
+#     output_dict = {"texts": txts, "boxes": boxes, "scores": scores}
+#     output_json = json.dumps(output_dict)  # Convert to JSON string
 
-    return output_json
+#     return output_json
+
+
+def index(image_url):
+    result = reader.readtext(image_url)
 
+    texts = []
+    probs = []
+
+    for (bbox, text, prob) in result:
+        # print(f'Text: {text}, Probability: {prob}')
+        texts.append(text)
+        probs.append(prob)
+
+    output_dict = {"texts": texts, "boxes": probs}
+    output_json = json.dumps(output_dict) 
+    
+    return output_json
 
 inputs_image_url = [
     gr.Textbox(type="text", label="Image URL"),
@@ -49,4 +63,4 @@ interface_image_url = gr.Interface(
     outputs=outputs_result_json,
     title="Text Extraction",
     cache_examples=False,
-).queue().launch()
+).queue().launch()

g-ocr.py

@@ -0,0 +1,63 @@
+# import cv2 
+# import pytesseract
+# import numpy as np
+
+# image = cv2.imread('rzse0mcqxbgs8z2pf6lr.png')
+
+# def get_grayscale(image):
+#     return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# def remove_noise(image):
+#     return cv2.medianBlur(image,5)
+ 
+# def thresholding(image):
+#     return cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
+
+# def dilate(image):
+#     kernel = np.ones((5,5),np.uint8)
+#     return cv2.dilate(image, kernel, iterations = 1)
+    
+# def erode(image):
+#     kernel = np.ones((5,5),np.uint8)
+#     return cv2.erode(image, kernel, iterations = 1)
+
+# def opening(image):
+#     kernel = np.ones((5,5),np.uint8)
+#     return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
+
+# def canny(image):
+#     return cv2.Canny(image, 100, 200)
+
+# def deskew(image):
+#     coords = np.column_stack(np.where(image > 0))
+#     angle = cv2.minAreaRect(coords)[-1]
+#     if angle < -45:
+#         angle = -(90 + angle)
+#     else:
+#         angle = -angle
+#     (h, w) = image.shape[:2]
+#     center = (w // 2, h // 2)
+#     M = cv2.getRotationMatrix2D(center, angle, 1.0)
+#     rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
+#     return rotated
+
+# def match_template(image, template):
+#     return cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED) 
+
+# custom_config = r'--oem 3 --psm 6'
+# pytesseract.image_to_string(image, config=custom_config)
+
+# gray = get_grayscale(image)
+# thresh = thresholding(gray)
+# opening = opening(gray)
+# canny = canny(gray)
+
+import easyocr
+
+image_url = 'https://res.cloudinary.com/ddvajyjou/image/upload/v1706960876/rzse0mcqxbgs8z2pf6lr.png'
+
+reader = easyocr.Reader(['en'])
+result = reader.readtext(image_url)
+
+for (bbox, text, prob) in result:
+    print(f'Text: {text}, Probability: {prob}')

requirements.txt

@@ -6,4 +6,8 @@ Shapely
 requests
 numpy
 pillow
-gradio
+gradio
+torch
+torchvision 
+torchaudio
+easyocr

tesseract-ocr-w64-setup-5.3.3.20231005.exe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79af1f9153b8ff988baffaa164fc70799950078f887e2c93dc3fa7efed674b21
+size 50159184