We describe the project REE-HDSC and outline our efforts to improve the
quality of named entities extracted automatically from texts generated by
hand-written text recognition (HTR) software. We describe a six-step processing
pipeline and test it by processing 19th and 20th century death certificates
from the civil registry of Curacao. We find that the pipeline extracts dates
with high precision but that the precision of person name extraction is low.
Next we show how name precision extraction can be improved by retraining HTR
models with names, post-processing and by identifying and removing incorrect
names.

我们介绍了 REE-HDSC 项目，并概述了我们努力提高手写文本识别软件生成的文本中自动提取命名实体的质量的工作。我们描述了一个六步处理流程，并通过处理库拉索民事登记处的 19 世纪和 20 世纪的死亡证明进行了测试。我们发现该流程能够高精度地提取日期，但人名提取的精度较低。接下来，我们展示了如何通过重新训练带有人名的 HTR 模型、后处理以及识别并删除错误名字来提高人名的提取精度。

REE-HDSC：历史数据库苏里南库拉索的实体识别

REE-HDSC: Recognizing Extracted Entities for the Historical Database  Suriname Curacao

Detection of curvilinear structures in images has long been of interest. One
of the most challenging aspects of this problem is inferring the graph
representation of the curvilinear network. Most existing delineation approaches
first perform binary segmentation of the image and then refine it using either
a set of hand-designed heuristics or a separate classifier that assigns
likelihood to paths extracted from the pixel-wise prediction. In our work, we
bridge the gap between segmentation and path classification by training a deep
network that performs those two tasks simultaneously. We show that this
approach is beneficial because it enforces consistency across the whole
processing pipeline. We apply our approach on roads and neurons datasets.

本文研究了识别图像中曲线结构的问题，提出了一种基于深度网络的方法，同时执行分割和路径分类这两个任务，以实现整个处理流程的连贯性，并在道路和神经元数据集上应用该方法。