Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes

TL;DR

This paper introduces a hybrid decoding method combining CTC and sequence-to-sequence models for handwritten text recognition, improving accuracy and efficiency by penalizing invalid paths during beam search.

Contribution

It proposes a novel CTC-Prefix-Score integration into S2S decoding, reducing model complexity while enhancing recognition performance.

Findings

Achieved 2.95% CER on IAM dataset with synthetic pretraining and language model

Model requires 10-20 times fewer parameters than state-of-the-art methods

Demonstrated competitive results across three HTR datasets

Abstract

In contrast to Connectionist Temporal Classification (CTC) approaches, Sequence-To-Sequence (S2S) models for Handwritten Text Recognition (HTR) suffer from errors such as skipped or repeated words which often occur at the end of a sequence. In this paper, to combine the best of both approaches, we propose to use the CTC-Prefix-Score during S2S decoding. Hereby, during beam search, paths that are invalid according to the CTC confidence matrix are penalised. Our network architecture is composed of a Convolutional Neural Network (CNN) as visual backbone, bidirectional Long-Short-Term-Memory-Cells (LSTMs) as encoder, and a decoder which is a Transformer with inserted mutual attention layers. The CTC confidences are computed on the encoder while the Transformer is only used for character-wise S2S decoding. We evaluate this setup on three HTR data sets: IAM, Rimes, and StAZH. On IAM, we achieve a competitive Character Error Rate (CER) of 2.95% when pretraining our model on synthetic data and including a character-based language model for contemporary English. Compared to other state-of-the-art approaches, our model requires about 10-20 times less parameters. Access our shared implementations via this link to GitHub: https://github.com/Planet-AI-GmbH/tfaip-hybrid-ctc-s2s.