# Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition

## TL;DR

This paper introduces an attention-based sequence-to-sequence model for handwritten text recognition, combining CNNs and RNNs, achieving competitive results without language models, and comparing various attention mechanisms.

## Contribution

It presents a novel end-to-end attention-based model for handwritten text recognition with extensive experimental analysis of attention mechanisms.

## Key findings

- Achieved competitive results on IAM and ICFHR2016 datasets.
- Compared various attention mechanisms and positional encodings.
- Significantly outperformed recent sequence-to-sequence approaches.

## Abstract

Encoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attention-based sequence-to-sequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained end-to-end and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without the use of a language model, and we significantly improve over any recent sequence-to-sequence approaches.