initial commit

apps.py

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+import gradio as gr
+from ocr_tamil.ocr import OCR
+
+model_path = r"parseq_tamil_v6.ckpt"
+text_detect_model = r"craft_mlt_25k.pth" # add the full path of the craft model
+ocr_detect = OCR(detect=True,tamil_model_path=model_path,detect_model_path=text_detect_model,enable_cuda=True)
+ocr_recognize = OCR(detect=False,tamil_model_path=model_path,detect_model_path=text_detect_model,enable_cuda=True)
+
+def predict(image_path,mode):
+    if mode == "recognize":
+        texts = ocr_recognize.predict(image_path)
+    else:
+        texts = ocr_detect.predict(image_path)
+    return texts
+
+
+image_examples = ["0.jpg","1.jpg","2.jpg","3.jpg","4.jpg","5.jpg",
+                  "6.jpg","7.jpg","8.jpg","9.jpg","10.jpg","14.jpg"]
+
+mode_examples = ["detect","recognize","recognize","recognize","recognize","recognize"
+                 ,"recognize","recognize","recognize","recognize","recognize","recognize"]
+
+input_1 = gr.Image(type="numpy")
+input_2 = gr.Radio(["recognize", "detect"], label="mode", 
+                   info="Only Text recognition or need both Text detection + recognition")
+
+examples = [[i,j] for i,j in zip(image_examples,mode_examples)]
+
+gr.Interface(
+    predict,
+    inputs=[input_1,input_2],
+    outputs=gr.TextArea(label="Extracted Text",interactive=False,
+                       show_copy_button=True),
+    title="OCR TAMIL",
+    examples=examples
+).launch()
+

craft_mlt_25k.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a5efbfb48b4081100544e75e1e2b57f8de3d84f213004b14b85fd4b3748db17
+size 83152330

parseq_tamil_v6.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:181ce656a72e3908c54fde2784466a359976ea026b9ab16d2d7ff44ecc68d4f6
+size 666822703

requirements.txt

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+ocr_tamil==0.1.3
+gradio