Fast writer adaptation with style extractor network for handwritten text recognition.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Writing style is a crucial but difficult-to-define attribute in handwritten text recognition (HTR).
• Existing HTR systems often struggle with writer variability, impacting overall accuracy.
• Writer adaptation methods aim to personalize HTR systems for individual writing styles to boost performance.

Purpose of the Study:

• To propose a general and fast writer adaptation solution within a deep learning framework.
• To develop a novel style extractor network (SEN) for explicitly capturing personalized writer information.
• To enhance the accuracy of writer-independent HTR systems by integrating extracted writing style.

Main Methods:

• A style extractor network (SEN) architecture comprising convolutional layers and gated recurrent units (GRUs) was designed.
• The SEN was trained using identification loss (IDL) to isolate writer-specific features from semantic content.
• Extracted style vectors were integrated into a writer-independent recognizer for fast, sentence-level adaptation.

Main Results:

• The proposed method achieved remarkable accuracy improvements in both Chinese and English offline handwritten text recognition tasks.
• A multi-information fusion network incorporating visual, context, and writing style features was validated on the HETR task.
• The fast adaptation method significantly outperformed previous multi-pass decoding techniques using only one line of adaptation data.

Conclusions:

• The proposed fast writer adaptation method effectively extracts and utilizes writing style information to enhance HTR accuracy.
• The SEN, trained with IDL, successfully isolates writer-specific attributes without complex pre-processing.
• This approach offers a computationally efficient and highly effective solution for personalizing HTR systems.