DOTA: Deformable Optimized Transformer Architecture for End-to-End Text Recognition with Retrieval-Augmented Generation

TL;DR

This paper presents a novel end-to-end text recognition framework combining ResNet, Vision Transformer, Deformable Convolutions, Retrieval-Augmented Generation, and CRF, achieving state-of-the-art accuracy across multiple benchmarks.

Contribution

It introduces a new architecture that integrates advanced modules for improved feature extraction and sequence modeling in OCR tasks, setting new performance standards.

Findings

Achieved over 97% accuracy on IC13 dataset.

Set new state-of-the-art results on six benchmark datasets.

Demonstrated robustness across diverse challenging datasets.

Abstract

Text recognition in natural images remains a challenging yet essential task, with broad applications spanning computer vision and natural language processing. This paper introduces a novel end-to-end framework that combines ResNet and Vision Transformer backbones with advanced methodologies, including Deformable Convolutions, Retrieval-Augmented Generation, and Conditional Random Fields (CRF). These innovations collectively enhance feature representation and improve Optical Character Recognition (OCR) performance. Specifically, the framework substitutes standard convolution layers in the third and fourth blocks with Deformable Convolutions, leverages adaptive dropout for regularization, and incorporates CRF for more refined sequence modeling. Extensive experiments conducted on six benchmark datasets IC13, IC15, SVT, IIIT5K, SVTP, and CUTE80 validate the proposed method's efficacy, achieving notable accuracies: 97.32% on IC13, 58.26% on IC15, 88.10% on SVT, 74.13% on IIIT5K, 82.17% on SVTP, and 66.67% on CUTE80, resulting in an average accuracy of 77.77%. These results establish a new state-of-the-art for text recognition, demonstrating the robustness of the approach across diverse and challenging datasets.