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No, PaddleOCR does not natively support batch inference directly using the How to Handle Batch Inference in PaddleOCRAlthough PaddleOCR doesn't natively process batches, you can implement batch inference by iterating over the batch and processing each image individually. Alternatively, you can modify the PaddleOCR codebase to support batch processing. Below are the recommended approaches: 1. Process Images SequentiallyModify your code to process each image in the batch one at a time. Here is an example: from paddleocr import PaddleOCR
from tqdm import tqdm
import numpy as np
# Initialize PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang="en")
# Example batch of images (np_dataset)
# np_dataset is a list of dictionaries with each dictionary containing an image
# np_dataset = [{'image': np_image1}, {'image': np_image2}, ...]
results = [] # To store OCR results for the batch
for item in tqdm(np_dataset, desc="Processing images"):
img = item['image'] # Extract the single image from the batch
result = ocr.ocr(img, cls=True) # Perform OCR
results.append(result) # Append the result to the listThis approach processes each image in the dataset sequentially and stores the OCR results in the 2. Use Multi-threading or Multi-processing for ParallelismIf you want to speed up the process, you can use multi-threading or multi-processing to process images in parallel. Here's an example using the from paddleocr import PaddleOCR
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
# Initialize PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang="en")
# Example batch of images (np_dataset)
# np_dataset = [{'image': np_image1}, {'image': np_image2}, ...]
def process_image(item):
img = item['image']
return ocr.ocr(img, cls=True)
# Use ThreadPoolExecutor for parallel processing
results = []
with ThreadPoolExecutor(max_workers=4) as executor: # Adjust max_workers as needed
futures = [executor.submit(process_image, item) for item in np_dataset]
for future in tqdm(futures, desc="Processing images"):
results.append(future.result())This approach processes images in parallel, significantly speeding up batch inference. 3. Modify PaddleOCR for Batch InferenceIf batch inference is a strict requirement, you can modify the PaddleOCR codebase to handle batches. Here's how:
However, this approach requires a deep understanding of the PaddleOCR codebase and might not be straightforward. Related Discussion: Multi-threading IssuesThe error you encountered during multi-threading (
For more information, refer to the GitHub discussion "多线程识别报错", which discusses multi-threading errors in PaddleOCR. Final Recommendation
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When I used the code snippet above, I got the following error:
itemin the for loop is a batch of images so it has four dimensions: [batch size, height, width, channel]. It seems like PaddleOCR() cannot handle batches. Could you please tell me how I can do batch inference? Thanks!Beta Was this translation helpful? Give feedback.
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