Duplicate from PranomVignesh/custom-tr-ocr

Co-authored-by: Vignesh Prakash <[email protected]>

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README.md

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+---
+title: Custom Tr Ocr
+emoji: 🦀
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 3.39.0
+app_file: app.py
+pinned: false
+duplicated_from: PranomVignesh/custom-tr-ocr
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import requests
+from PIL import Image
+
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("ericvo/scribbl-scan-trocr")
+
+# load image examples
+urls = ['https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg', 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU',
+        'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU']
+for idx, url in enumerate(urls):
+  image = Image.open(requests.get(url, stream=True).raw)
+  image.save(f"image_{idx}.png")
+
+def process_image(image):
+    # prepare image
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+
+    # generate (no beam search)
+    generated_ids = model.generate(pixel_values)
+
+    # decode
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    return generated_text
+
+title = "TrOCR - Fine tuned on IAM Dataset"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>"
+examples =[
+    ["./examples/image_0.png"],
+    ["./examples/image_1.png"], 
+    ["./examples/image_2.png"]
+]
+
+#css = """.output_image, .input_image {height: 600px !important}"""
+
+iface = gr.Interface(
+    fn=process_image, 
+    inputs=gr.inputs.Image(type="pil"), 
+    outputs=gr.outputs.Textbox(),
+    title=title,
+    live=True
+                         # examples=examples
+)
+iface.launch(debug=True)

requirements.txt

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+gradio
+transformers
+torch
+Pillow