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 inferenceback-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-CrossEntropy.