European libraries and archives are filled with enciphered manuscripts from
the early modern period. These include military and diplomatic correspondence,
records of secret societies, private letters, and so on. Although they are
enciphered with classical cryptographic algorithms, their contents are
unavailable to working historians. We therefore attack the problem of
automatically converting cipher manuscript images into plaintext. We develop
unsupervised models for character segmentation, character-image clustering, and
decipherment of cluster sequences. We experiment with both pipelined and joint
models, and we give empirical results for multiple ciphers.

该论文围绕密码手稿展开，运用经典密码算法，发展出无监督模型实现字符分割、图像聚类和破解密文等多种技术，可自动将手稿图像转化为文本。

历史手稿图像解密

Decipherment of Historical Manuscript Images

In Codice Ratio is a research project to study tools and techniques for
analyzing the contents of historical documents conserved in the Vatican Secret
Archives (VSA). In this paper, we present our efforts to develop a system to
support the transcription of medieval manuscripts. The goal is to provide
paleographers with a tool to reduce their efforts in transcribing large
volumes, as those stored in the VSA, producing good transcriptions for
significant portions of the manuscripts. We propose an original approach based
on character segmentation. Our solution is able to deal with the dirty
segmentation that inevitably occurs in handwritten documents. We use a
convolutional neural network to recognize characters and language models to
compose word transcriptions. Our approach requires minimal training efforts,
making the transcription process more scalable as the production of training
sets requires a few pages and can be easily crowdsourced. We have conducted
experiments on manuscripts from the Vatican Registers, an unreleased corpus
containing the correspondence of the popes. With training data produced by 120
high school students, our system has been able to produce good transcriptions
that can be used by paleographers as a solid basis to speedup the transcription
process at a large scale.

该研究项目提出了一种基于字符分割的新方法，使用卷积神经网络识别字符和语言模型合成单词转录，从而为停泊罗斯的手写文档大量转录节省时间和努力。

探索梵蒂冈秘密档案中的知识发现 ——《In Codice Ratio》第一集：手稿的机器转录

Towards Knowledge Discovery from the Vatican Secret Archives. In Codice  Ratio -- Episode 1: Machine Transcription of the Manuscripts

Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector. The Convolutional Neural Networks
(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust
under different conditions (e.g., variations in camera, lighting, and
background). Specially for character segmentation and recognition, we design a
two-stage approach employing simple data augmentation tricks such as inverted
License Plates (LPs) and flipped characters. The resulting ALPR approach
achieved impressive results in two datasets. First, in the SSIG dataset,
composed of 2,000 frames from 101 vehicle videos, our system achieved a
recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better
than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,
respectively) and considerably outperforming previous results (81.80%). Second,
targeting a more realistic scenario, we introduce a larger public dataset,
called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos
and 4,500 frames captured when both camera and vehicles are moving and also
contains different types of vehicles (cars, motorcycles, buses and trucks). In
our proposed dataset, the trial versions of commercial systems achieved
recognition rates below 70%. On the other hand, our system performed better,
with recognition rate of 78.33% and 35 FPS.

本文提出了基于 YOLO 名物体检测器的自动车牌识别系统，使用卷积神经网络 (CNN) 对每个车牌识别阶段进行训练和微调，实现在不同条件下具有稳健性。针对字符分割和识别，本文设计了一个两阶段方法，并采用倒置车牌和翻转字符等简单的数据增强技巧。该系统在两个数据集中均取得了卓越的成绩。

基于 YOLO 检测器的鲁棒的实时自动车牌识别

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO  Detector

OCR character segmentation for multilingual printed documents is difficult
due to the diversity of different linguistic characters. Previous approaches
mainly focus on monolingual texts and are not suitable for multilingual-lingual
cases. In this work, we particularly tackle the Chinese/English mixed case by
reframing it as a semantic segmentation problem. We take advantage of the
successful architecture called fully convolutional networks (FCN) in the field
of semantic segmentation. Given a wide enough receptive field, FCN can utilize
the necessary context around a horizontal position to determinate whether this
is a splitting point or not. As a deep neural architecture, FCN can
automatically learn useful features from raw text line images. Although trained
on synthesized samples with simulated random disturbance, our FCN model
generalizes well to real-world samples. The experimental results show that our
model significantly outperforms the previous methods.

本研究旨在解决 OCR 字符分割在多语言多样性的问题上，尤其关注中英文混合场景，通过全卷积网络架构进行语义分割，能够利用水平轴周围的上下文确定分割点，从而在合成样本与真实世界样本中实现更优的效果。