EASTER: Efficient and Scalable Text Recognizer

TL;DR

EASTER introduces a convolutional, non-recurrent OCR model that is efficient, scalable, and achieves competitive or superior results on standard datasets for both printed and handwritten text recognition.

Contribution

The paper proposes a novel 1-D convolutional OCR model that simplifies architecture, enables parallel training, and outperforms RNN-based models on key benchmarks.

Findings

Outperforms RNN-based models on IIIT-5k and SVT datasets

Achieves state-of-the-art results in handwritten text recognition

Uses synthetic data generation for training augmentation

Abstract

Recent progress in deep learning has led to the development of Optical Character Recognition (OCR) systems which perform remarkably well. Most research has been around recurrent networks as well as complex gated layers which make the overall solution complex and difficult to scale. In this paper, we present an Efficient And Scalable TExt Recognizer (EASTER) to perform optical character recognition on both machine printed and handwritten text. Our model utilises 1-D convolutional layers without any recurrence which enables parallel training with considerably less volume of data. We experimented with multiple variations of our architecture and one of the smallest variant (depth and number of parameter wise) performs comparably to RNN based complex choices. Our 20-layered deepest variant outperforms RNN architectures with a good margin on benchmarking datasets like IIIT-5k and SVT. We also showcase improvements over the current best results on offline handwritten text recognition task. We also present data generation pipelines with augmentation setup to generate synthetic datasets for both handwritten and machine printed text.