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Document Language Class Ar En Zh
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Document Language Class Ar En Zh

Developed by ernie-ai
An image classification model built with PyTorch and HuggingPics, specifically designed to recognize document language types (Arabic, English, Chinese).
Image Classification
Transformers
#Multilingual Document Classification#81.1% Accuracy#Text Orientation Recognition
Downloads 18
Release Time : 2/7/2023

Model Overview

This model uses image classification technology to automatically identify the language type in document images, supporting classification for Arabic, English, and Chinese.

Model Features

Multilingual Document Recognition
Accurately identifies document images in Arabic, English, and Chinese.
High Accuracy
Achieves 81.11% accuracy on test datasets with stable performance.
Easy to Use
Built with the HuggingPics framework, supporting quick deployment and integration.

Model Capabilities

Image Classification
Multilingual Document Recognition

Use Cases

Document Processing
Multilingual Document Classification
Automatically classifies scanned document images into Arabic, English, or Chinese.
Accuracy reaches 81.11%
Archive Management
Helps libraries or archives automate the classification and organization of multilingual documents.
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