Trocr Large Printed
Transformer-based optical character recognition model for single-line printed text recognition
Downloads 295.59k
Release Time : 3/2/2022
Model Overview
TrOCR adopts an encoder-decoder architecture combining image Transformer and text Transformer, specifically designed for optical character recognition (OCR) tasks. This version is optimized for printed text.
Model Features
Hybrid Architecture Design
Combines visual Transformer encoder and text Transformer decoder for end-to-end OCR
Pre-trained Weight Initialization
Image encoder inherits BEiT weights, text decoder inherits RoBERTa weights, enhancing model performance
Printed Text Optimization
Specifically fine-tuned for printed text recognition, demonstrating excellent performance on the SROIE dataset
Model Capabilities
Printed text recognition
Single-line text image processing
End-to-end character recognition
Use Cases
Document Digitization
Receipt Recognition
Automatically recognize text information in scanned receipts
Performs well on the SROIE dataset
Form Processing
Extract text content from form documents
Industrial Applications
Product Label Recognition
Automatically read printed text on product labels
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