SomeAI.org
  • Hot AI Tools
  • New AI Tools
  • AI Category
SomeAI.org
SomeAI.org

Discover 10,000+ free AI tools instantly. No login required.

About

  • Blog

© 2025 • SomeAI.org All rights reserved.

  • Privacy Policy
  • Terms of Service
Home
Extract text from scanned documents
LayoutLM DocVQA x PaddleOCR

LayoutLM DocVQA x PaddleOCR

Extract text from images using OCR

You May Also Like

View All
🏢

Pdf2text

Extract text from PDF and answer questions

0
🏆

1853ArchiveOCR

OCR Tool for the 1853 Archive Site

0
🐠

Invoice Extractor

Extract text from multilingual invoices

4
📸

OCR Image To Text

Extract text from images using OCR

0
🌍

HSN Explanatory Notes Bot

Find information using text queries

0
💻

TextScan

Extract handwritten text from images

0
🏆

Chatbox

Search documents using semantic queries

0
🏥

Medical Ner App

Extract named entities from medical text

3
🚀

Chat With Documents

Upload and query documents for information extraction

0
🏢

OCR MULTI

Extract text from images

0
💻

Ocr Image File Processing

Upload and analyze documents for text extraction and Q&A

1
📜

Historical OCR

Employs Mistral OCR for transcribing historical data

1

What is LayoutLM DocVQA x PaddleOCR ?

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.

Features

  • Advanced Layout Understanding: Captures the spatial structure of text in documents, enabling context-aware text extraction.
  • Multi-Language Support: Recognizes text in multiple languages, making it versatile for global document processing.
  • High Accuracy OCR: Utilizes PaddleOCR's state-of-the-art text recognition capabilities for precise text extraction.
  • End-to-End Processing: Seamlessly processes document images from input to structured text output.
  • Pre-Trained Models: Built on pre-trained models for reliable performance without requiring extensive manual training.

How to use LayoutLM DocVQA x PaddleOCR ?

  1. Install Required Packages: Install PaddleOCR and LayoutLM libraries to access the combined functionality.
  2. Load Pre-Trained Models: Load the LayoutLM model for document layout analysis and PaddleOCR for text recognition.
  3. Preprocess the Image: Input the scanned document image and apply necessary preprocessing steps.
  4. Detect Text Regions: Use LayoutLM to identify text regions within the document.
  5. Extract Text: Apply PaddleOCR to extract text from the identified regions.
  6. Parse and Structure Text: Combine and format the extracted text into a readable output.
# 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)

Frequently Asked Questions

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.

Recommended Category

View All
👗

Try on virtual clothes

📄

Document Analysis

🔖

Put a logo on an image

💻

Code Generation

🎵

Generate music

📐

Convert 2D sketches into 3D models

😊

Sentiment Analysis

🌈

Colorize black and white photos

🎥

Convert a portrait into a talking video

🖌️

Generate a custom logo

📋

Text Summarization

🔊

Add realistic sound to a video

💬

Add subtitles to a video

😂

Make a viral meme

🎨

Style Transfer