While OCR has been used in various applications, its output is not always
accurate, leading to misfit words. This research work focuses on improving the
optical character recognition (OCR) with ML techniques with integration of OCR
with long short-term memory (LSTM) based sequence to sequence deep learning
models to perform document translation. This work is based on ANKI dataset for
English to Spanish translation. In this work, I have shown comparative study
for pre-trained OCR while using deep learning model using LSTM-based seq2seq
architecture with attention for machine translation. End-to-end performance of
the model has been expressed in BLEU-4 score. This research paper is aimed at
researchers and practitioners interested in OCR and its applications in
document translation.

本研究旨在提高 OCR 质量，通过使用 LSTM 技术集成 OCR 与深度学习模型进行文档翻译，并在英语到西班牙语翻译中显示了深度学习模型的比较研究，将终端到终端的性能用 BLEU-4 评分表达，旨在让研究人员和 OCR 应用于文档翻译的实践者感兴趣。

TransDocs：基于光学字符识别和逐字翻译的翻译工具

TransDocs: Optical Character Recognition with word to word translation

Neural Sign Language Production (SLP) aims to automatically translate from
spoken language sentences to sign language videos. Historically the SLP task
has been broken into two steps; Firstly, translating from a spoken language
sentence to a gloss sequence and secondly, producing a sign language video
given a sequence of glosses. In this paper we apply Natural Language Processing
techniques to the first step of the SLP pipeline. We use language models such
as BERT and Word2Vec to create better sentence level embeddings, and apply
several tokenization techniques, demonstrating how these improve performance on
the low resource translation task of Text to Gloss. We introduce Text to
HamNoSys (T2H) translation, and show the advantages of using a phonetic
representation for sign language translation rather than a sign level gloss
representation. Furthermore, we use HamNoSys to extract the hand shape of a
sign and use this as additional supervision during training, further increasing
the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of
26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art
baselines.

本文旨在将自然语言处理技术应用于 Sign Language Production 管道的第一步，介绍了一种基于音素表示而非基于 gloss 表示的 T2H 翻译方法，并使用 HamNoSys 提取标志的手型作为额外的监督来进一步提高性能，最终在两个数据集上均取得了 BLEU-4 得分的最佳表现。

转变表征：探究神经手语语言表征

Changing the Representation: Examining Language Representation for Neural Sign Language Production

In this paper, we present a generative model to generate a natural language
sentence describing a table region, e.g., a row. The model maps a row from a
table to a continuous vector and then generates a natural language sentence by
leveraging the semantics of a table. To deal with rare words appearing in a
table, we develop a flexible copying mechanism that selectively replicates
contents from the table in the output sequence. Extensive experiments
demonstrate the accuracy of the model and the power of the copying mechanism.
On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the
current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to
39.12, respectively. Furthermore, we introduce an open-domain dataset
WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our
model achieves a BLEU-4 score of 38.23, which outperforms template based and
language model based approaches.

本文提出了一种生成模型，它将一行从表中映射到一个连续向量，然后通过利用表的语义生成一个自然语言句子，还提出了一种灵活的复制机制以处理表中出现的罕见单词，并在两个综合数据集上取得了较高 BLEU-4 分数，其中一个数据集包含 13,318 个解释句子和 4,962 个表格。