Latin has historically led the state-of-the-art in handwritten optical
character recognition (OCR) research. Adapting existing systems from Latin to
alpha-syllabary languages is particularly challenging due to a sharp contrast
between their orthographies. The segmentation of graphical constituents
corresponding to characters becomes significantly hard due to a cursive writing
system and frequent use of diacritics in the alpha-syllabary family of
languages. We propose a labeling scheme based on graphemes (linguistic segments
of word formation) that makes segmentation in-side alpha-syllabary words linear
and present the first dataset of Bengali handwritten graphemes that are
commonly used in an everyday context. The dataset contains 411k curated samples
of 1295 unique commonly used Bengali graphemes. Additionally, the test set
contains 900 uncommon Bengali graphemes for out of dictionary performance
evaluation. The dataset is open-sourced as a part of a public Handwritten
Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for
multi-target grapheme classification. The unique graphemes present in this
dataset are selected based on commonality in the Google Bengali ASR corpus.
From competition proceedings, we see that deep-learning methods can generalize
to a large span of out of dictionary graphemes which are absent during
training. Dataset and starter codes at www.kaggle.com/c/bengaliai-cv19.

本研究提出了一种基于字形标注方案来线性切分字形组成部分的 标注方法，给出了一份 孟加拉语手写字母片段的数据集，用于测试视觉算法的多目标字形分类，结果表明，深度学习方法可以推广到许多字典之外的字形。

常见孟加拉手写字母的大型多目标数据集

A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes

Due to the fact that Korean is a highly agglutinative, character-rich
language, previous work on Korean morphological analysis typically employs the
use of sub-character features known as graphemes or otherwise utilizes
comprehensive prior linguistic knowledge (i.e., a dictionary of known
morphological transformation forms, or actions). These models have been created
with the assumption that character-level, dictionary-less morphological
analysis was intractable due to the number of actions required. We present, in
this study, a multi-stage action-based model that can perform morphological
transformation and part-of-speech tagging using arbitrary units of input and
apply it to the case of character-level Korean morphological analysis. Among
models that do not employ prior linguistic knowledge, we achieve
state-of-the-art word and sentence-level tagging accuracy with the Sejong
Korean corpus using our proposed data-driven Bi-LSTM model.

本研究提出了一种基于多阶段行动的模型，可对韩语进行形态学转换和词性标注，使用双向长短时记忆网络模型对 Sejong 韩国语语料库实现了词和句子级别的标记精度，达到了无先验语言知识的同类模型的最新 交状态。

韩语形态分析和词性标注的丰富字符级信息

Rich Character-Level Information for Korean Morphological Analysis and  Part-of-Speech Tagging

We investigate training end-to-end speech recognition models with the
recurrent neural network transducer (RNN-T): a streaming, all-neural,
sequence-to-sequence architecture which jointly learns acoustic and language
model components from transcribed acoustic data. We explore various model
architectures and demonstrate how the model can be improved further if
additional text or pronunciation data are available. The model consists of an
`encoder', which is initialized from a connectionist temporal
classification-based (CTC) acoustic model, and a `decoder' which is partially
initialized from a recurrent neural network language model trained on text data
alone. The entire neural network is trained with the RNN-T loss and directly
outputs the recognized transcript as a sequence of graphemes, thus performing
end-to-end speech recognition. We find that performance can be improved further
through the use of sub-word units (`wordpieces') which capture longer context
and significantly reduce substitution errors. The best RNN-T system, a
twelve-layer LSTM encoder with a two-layer LSTM decoder trained with 30,000
wordpieces as output targets achieves a word error rate of 8.5\% on
voice-search and 5.2\% on voice-dictation tasks and is comparable to a
state-of-the-art baseline at 8.3\% on voice-search and 5.4\% voice-dictation.

本研究使用循环神经网络传输器（RNN-T）来训练端到端语音识别模型，并探索了不同的模型架构。通过使用字元素（wordpieces），模型的性能进一步得到了提高，最终在语音搜索和语音听写任务上取得了与最新基线相当的成果。