Generating Synthetic Data for Text Recognition

Abstract

Generating synthetic images is an art which emulates the natural process of image generation in a closest possible manner. In this work, we exploit such a framework for data generation in handwritten domain. We render synthetic data using open source fonts and incorporate data augmentation schemes. As part of this work, we release 9M synthetic handwritten word image corpus which could be useful for training deep network architectures and advancing the performance in handwritten word spotting and recognition tasks.

5 pages, 2 figures

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• On Rendering Synthetic Images for Training an Object Detector

Cited by in corpus (6)

• TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
• A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification
• Unconstrained Scene Text and Video Text Recognition for Arabic Script
• GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
• Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture
• UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese