1. Tutorial Introduction

InkSight is a revolutionary artificial intelligence technology launched by Google Research in 2024. It is specifically designed for the recognition and digitization of handwritten text. The research findings are... InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write The core advantage of this technology lies in its unique learning method. By mimicking the human reading and learning process, it continuously rewrites and learns handwritten text, thereby accumulating an understanding of the appearance and meaning of characters. Compared to traditional optical character recognition (OCR) technology, InkSight demonstrates higher recognition accuracy when processing handwritten text in complex backgrounds, blurry conditions, or low-light conditions.

This tutorial contains 2 functions:

• Word-level transcription: This feature supports word-level transcription, where the input image is converted into a single word and the output is InkSight.
• Full page transcription: This feature supports the whole page level. The input image can be the entire writing page, and the output is the full InkSight.

InkSight has a very high recognition accuracy. Experiments show that humans can read the text tracing generated by InkSight with an accuracy of up to 87%, and more than two-thirds of the tracing results are almost indistinguishable from real handwriting. This means that InkSight can not only recognize handwritten text, but also restore the handwritten content with extremely high accuracy, which is a great boon for users who like to record handwriting.

InkSight also shows great potential in the field of cultural heritage protection. It can effectively digitize precious handwritten documents, facilitate historical research, and protect and pass on languages and cultures that are less digitized.

InkSight's technology not only surpasses traditional OCR technology, but also brings new breakthroughs in the field of handwriting recognition. It uses modern machine learning technologies such as deep learning and neural networks to make the recognition of handwritten text more flexible and powerful. The launch of this technology may trigger a race for more innovations and breakthroughs in the handwriting recognition track.

2. Operation steps

After starting the container, wait for about 5 seconds to load the model, and click the API address to enter the web interface.

1. Word transcription

• Select Word-level inference
• Upload a single word handwritten
• Click ink to render
• View the rendering results

2. Full page transcription

• Select Full page inference
• Upload a full page of handwriting
• Click ink to render
• View the rendering results

Communication

🖌️ If you see a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome friends to scan the QR code and remark [SD Tutorial] to join the group to discuss various technical issues and share application effects↓