# Aggregation Cross-Entropy for Sequence Recognition

## TL;DR

This paper introduces aggregation cross-entropy (ACE), a new loss function for sequence recognition that is faster, simpler, and more versatile than existing methods like CTC and attention, with broad potential applications.

## Contribution

The paper presents ACE, a novel sequence recognition loss that is easier to implement, faster, requires less memory, and can be applied to 2D predictions and counting tasks.

## Key findings

- ACE achieves competitive performance with CTC and attention.
- ACE implementation involves only four formulas, enabling quick deployment.
- ACE is suitable for 2D prediction and counting problems.

## Abstract

In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective. The ACE loss function exhibits competitive performance to CTC and the attention mechanism, with much quicker implementation (as it involves only four fundamental formulas), faster inference\back-propagation (approximately O(1) in parallel), less storage requirement (no parameter and negligible runtime memory), and convenient employment (by replacing CTC with ACE). Furthermore, the proposed ACE loss function exhibits two noteworthy properties: (1) it can be directly applied for 2D prediction by flattening the 2D prediction into 1D prediction as the input and (2) it requires only characters and their numbers in the sequence annotation for supervision, which allows it to advance beyond sequence recognition, e.g., counting problem. The code is publicly available at https://github.com/summerlvsong/Aggregation-Cross-Entropy.