Extract text from images using OCR
Using Paddleocr to extract information from billing receipt
Extract named entities from text
Find similar text segments based on your query
Find similar sentences in your text using search queries
Extract key entities from text queries
Answer questions based on provided text
Analyze documents to extract and structure text
Extract handwritten text from images
Upload and analyze documents for text extraction and Q&A
GOT - OCR (from : UCAS, Beijing)
Extract text from documents
Process text to extract entities and details
LayoutLM DocVQA x PaddleOCR is a powerful tool designed to extract text from scanned documents. It combines the capabilities of LayoutLM, a pre-trained model for document visual question answering, and PaddleOCR, a robust OCR (Optical Character Recognition) system. This integration enables accurate text extraction from images of documents, leveraging advanced layout understanding and text recognition technologies.
# Example usage:
from paddlexOCR import PaddleOCR
from layoutlm import Document
# Initialize models
ocr = PaddleOCR(lang='en')
document = Document.from_file("document.pdf")
# Process document
text_regions = document.analyze_layout()
extracted_text = ocr.ocr(text_regions)
# Output the result
print(extracted_text)
What formats does LayoutLM DocVQA x PaddleOCR support?
It supports PDF, JPEG, PNG, and BMP formats for document processing.
Can it handle handwritten text?
While it is primarily designed for printed text, it may have limited success with clear, high-quality handwritten text.
Is it suitable for multi-language documents?
Yes, it supports multiple languages, including English, Chinese, French, German, and many others, thanks to PaddleOCR's multi-language capabilities.