TS-Net: OCR Trained to Switch Between Text Transcription Styles

Abstract

Users of OCR systems, from different institutions and scientific disciplines, prefer and produce different transcription styles. This presents a problem for training of consistent text recognition neural networks on real-world data. We propose to extend existing text recognition networks with a Transcription Style Block (TSB) which can learn from data to switch between multiple transcription styles without any explicit knowledge of transcription rules. TSB is an adaptive instance normalization conditioned by identifiers representing consistently transcribed documents (e.g. single document, documents by a single transcriber, or an institution). We show that TSB is able to learn completely different transcription styles in controlled experiments on artificial data, it improves text recognition accuracy on large-scale real-world data, and it learns semantically meaningful transcription style embedding. We also show how TSB can efficiently adapt to transcription styles of new documents from transcriptions of only a few text lines.

References in corpus (5)

• A Learned Representation For Artistic Style
• Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
• Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
• Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
• Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Cited by in corpus (1)

• A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets