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
this https URL

本文提出一种新方法聚合交叉熵（ACE）用于序列识别，其性能与 CTC 和注意力机制不相上下，但实现速度更快、存储要求更低、更便于使用，并能直接应用于 2D 预测及计数问题。

序列识别的聚合交叉熵

Aggregation Cross-Entropy for Sequence Recognition

We describe an online handwriting system that is able to support 102
languages using a deep neural network architecture. This new system has
completely replaced our previous Segment-and-Decode-based system and reduced
the error rate by 20%-40% relative for most languages. Further, we report new
state-of-the-art results on IAM-OnDB for both the open and closed dataset
setting. The system combines methods from sequence recognition with a new input
encoding using B\'ezier curves. This leads to up to 10x faster recognition
times compared to our previous system. Through a series of experiments we
determine the optimal configuration of our models and report the results of our
setup on a number of additional public datasets.

本文介绍了一种支持 102 种语言的在线手写系统，采用深度神经网络架构和贝塞尔曲线的新输入编码，与之前的系统相比降低了 20%-40% 的错误率，并在 IAM-OnDB 数据集上取得了新的最优结果。通过实验确定了模型的最优配置，并在多个公共数据集上进行了评估。

基于 LSTM 的快速多语言在线手写识别

Fast Multi-language LSTM-based Online Handwriting Recognition

Recognizing text in natural images is a challenging task with many unsolved
problems. Different from those in documents, words in natural images often
possess irregular shapes, which are caused by perspective distortion, curved
character placement, etc. We propose RARE (Robust text recognizer with
Automatic REctification), a recognition model that is robust to irregular text.
RARE is a specially-designed deep neural network, which consists of a Spatial
Transformer Network (STN) and a Sequence Recognition Network (SRN). In testing,
an image is firstly rectified via a predicted Thin-Plate-Spline (TPS)
transformation, into a more "readable" image for the following SRN, which
recognizes text through a sequence recognition approach. We show that the model
is able to recognize several types of irregular text, including perspective
text and curved text. RARE is end-to-end trainable, requiring only images and
associated text labels, making it convenient to train and deploy the model in
practical systems. State-of-the-art or highly-competitive performance achieved
on several benchmarks well demonstrates the effectiveness of the proposed
model.

我们提出了 RARE（具有自动矫正的强韧文本识别器），这是一种专门设计的深度神经网络，用于识别不规则文本，并能在多种基准测试上取得最先进或高度竞争性的表现。