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from hezar.models import Model
from hezar.utils import load_image, draw_boxes
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import gradio as gr
import numpy as np
from PIL import Image
# Load models on CPU (Hugging Face Spaces default)
craft_model = Model.load("hezarai/CRAFT", device="cpu")
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
trocr_model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
def recognize_handwritten_text(image):
# Convert Gradio image to format compatible with hezar
image_np = np.array(image)
processed_image = load_image(image_np)
# Detect text regions with CRAFT
outputs = craft_model.predict(processed_image)
if not outputs or "boxes" not in outputs[0]:
return Image.fromarray(processed_image), "No text detected"
boxes = outputs[0]["boxes"]
pil_image = Image.fromarray(processed_image)
texts = []
# Recognize text in each detected region
for box in boxes:
x_min, y_min, x_max, y_max = box[0][0], box[0][1], box[2][0], box[2][1]
crop = pil_image.crop((x_min, y_min, x_max, y_max))
pixel_values = processor(images=crop, return_tensors="pt").pixel_values
generated_ids = trocr_model.generate(pixel_values)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
texts.append(text)
# Draw boxes on the image
result_image = draw_boxes(processed_image, boxes)
result_pil = Image.fromarray(result_image)
# Join recognized texts
text_data = " ".join(texts) if texts else "No text recognized"
return result_pil, f"Recognized text: {text_data}"
# Create Gradio interface
interface = gr.Interface(
fn=recognize_handwritten_text,
inputs=gr.Image(type="pil"),
outputs=[gr.Image(type="pil"), gr.Text()],
title="Handwritten Text Detection and Recognition",
description="Upload an image to detect and recognize handwritten text."
)
# Launch the app
interface.launch() |