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This project provides a production-ready multimodal NER system that combines text models like BERT and RoBERTa with vision-language models such as CLIP and BLIP to enable joint entity extraction from text and images, supporting multiple fusion mechanisms and a complete evaluation system.
Section 01
This project provides a production-ready multimodal NER system that combines text models like BERT and RoBERTa with vision-language models such as CLIP and BLIP to enable joint entity extraction from text and images, supporting multiple fusion mechanisms and a complete evaluation system.
Section 02
Named Entity Recognition (NER) is a fundamental task in natural language processing, aiming to identify entities such as person names, place names, and organization names from text. Traditional NER systems rely solely on text input, but in real-world scenarios, we often have both text and image information—such as social media posts, images accompanying news articles, scanned documents, etc.
Multimodal NER has emerged as a solution; it processes both text and visual information simultaneously and improves the accuracy and robustness of entity recognition through cross-modal fusion. The project introduced in this article provides a production-ready implementation of multimodal NER, based on PyTorch and modern Transformer architectures.
Section 03
The project adopts a modular design, with core components including:
Section 04
Section 05
The project implements various unimodal and multimodal models:
Text Encoders:
Vision Encoders:
Multimodal Fusion Strategies:
Section 06
The project provides comprehensive evaluation metrics:
Section 07
A user posts on Twitter: "Musk announces a new plan at SpaceX headquarters" with an image. Pure text NER can recognize "Musk" and "SpaceX", but if the accompanying image is a photo of Musk at a Tesla factory, visual information can help verify or correct the entity recognition results.
Section 08
In scanned business contracts, the person's name in the signature area may be difficult to recognize accurately via OCR, but combining the visual features of the signature image can improve recognition accuracy.
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