<b>Student Messy Handwritten Dataset (SMHD) </b>

Within the central repository, there are subfolders of different categories. Each of these subfolders contains both images and their corresponding transcriptions, saved as .txt files. As an example, the folder 'summary-based-0001-0055' encompasses 55 handwritten image documents pertaining to the summary task, with the images ranging from 0001 to 0055 within this category. In the transcription files, any crossed-out content is denoted by the '#' symbol, facilitating the easy identification of files with or without such modifications.Moreover, there exists a document detailing the transcription rules utilized for transcribing the dataset. Following these guidelines will enable the seamless addition of more images.<b>Dataset Description:</b>We have incorporated contributions from more than 500 students to construct the dataset. Handwritten examination papers are primary sources in academic institutes to assess student learning. In our experience as academics, we have found that student examination papers tend to be messy with all kinds of insertions and corrections and would thus be a great source of documents for investigating HTR in the wild. Unfortunately, student examination papers are not available due to ethical considerations. So, we created an exam-like situation to collect handwritten samples from students. The corpus of the collected data is academic-based. Usually, in academia, handwritten papers have lines in them. For this purpose, we drew lines using light colors on white paper. The height of a line is 1.5 pt and the space between two lines is 40 pt. The filled handwritten documents were scanned at a resolution of 300 dpi at a grey-level resolution of 8 bits.<b>Collection Process</b>: The collection process was done in four different ways. <b>In the first exercise</b>, we asked participants to summarize a given text in their own words. We called it a summary-based dataset. In the summary writing task, we included 60 undergraduate students studying the English language as a subject. After getting their consent, we distributed printed text articles and we asked them to choose one article, read it and summarize it in a paragraph in 15 minutes. The corpus of the printed text articles given to the participants was collected from the Internet on different topics. The articles were related to current political situations, daily life activities, and the Covid-19 pandemic.<b>In the second exercise</b>, we asked participants to write an essay from a given list of topics, or they could write on any topic of their choice. We called it an essay-based dataset. This dataset is collected from 250 High school students. We gave them 30 minutes to think about the topic and write for this task.<b>In the third exercise</b>, we select participants from different subjects and ask them to write on a topic from their current study. We called it a subject-based dataset. For this study, we used undergraduate students from different subjects, including 33 students from Mathematics, 71 from Biological Sciences, 24 from Environmental Sciences, 17 from Physics, and more than 84 from English studies.Finally a <b>class-notes dataset,</b> we have collected class notes from almost 31 students on the same topic. We asked students to take notes of every possible sentence the speaker delivered during the lecture. After finishing the lesson in almost 10 minutes, we asked students to recheck their notes and compare them with other classmates. We did not impose any time restrictions for rechecking. We observed more cross-outs and corrections in class-notes compared to summary-based and academic-based collections.In all four exercises, we did not impose any rules on them, for example, spacing, usage of a pen, etc. We asked them to cross out the text if it seemed inappropriate. Although usually writers made corrections in a second read, we also gave an extra 5 minutes for correction purposes.

本中央存储库包含多个分类子文件夹，每个子文件夹均存储有图像文件及其对应的转录文本（均保存为.txt格式文件）。以'summary-based-0001-0055'文件夹为例，该文件夹包含55份与摘要任务相关的手写图像文档，该类别下的图像编号范围为0001至0055。转录文件中，所有被划去的内容均以'#'符号标注，便于快速区分存在修改与无修改的文件。此外，本数据集附带一份详细说明转录规则的文档，遵循该规则可实现新增图像的无缝接入。<b>数据集说明：</b>本数据集共吸纳超500名学生参与共建。手写试卷是学术机构评估学生学习成果的核心来源，据我们作为学术工作者的经验来看，学生手写试卷往往存在大量插补与涂改痕迹，是开展野外手写文本识别（Handwritten Text Recognition，HTR）研究的优质文档来源。但受伦理约束，学生真实手写试卷无法公开获取，因此我们搭建了类考试场景以收集学生手写样本。本次采集的数据语料均为学术类内容：学术场景下的手写文稿通常带有印刷格线，为此我们在白纸上以浅色绘制辅助格线，单条格线高度为1.5 pt，两行格线间距为40 pt。填写完成的手写文档均以300 dpi分辨率、8位灰度级进行扫描存档。<b>采集流程：</b>本次数据采集共分为四种不同形式：<b>第一类任务：摘要写作任务</b>我们要求参与者以自身语言复述给定文本，该类数据集被命名为摘要类数据集。本次共有60名英语专业本科生参与该任务：在征得参与者同意后，我们分发打印好的文本文章，要求参与者任选一篇进行阅读，并在15分钟内将其浓缩为一段摘要。本次使用的打印文本语料均取自互联网，涵盖时政热点、日常生活及新冠疫情相关主题。<b>第二类任务：随笔写作任务</b>我们要求参与者从给定主题列表中任选其一，或自选主题完成一篇随笔，该类数据集被命名为随笔类数据集。本次采集对象为250名高中生，给予他们30分钟用于构思并完成写作。<b>第三类任务：学科写作任务</b>我们从不同学科中招募参与者，要求他们围绕当前正在学习的学科主题完成写作，该类数据集被命名为学科类数据集。本次参与的学生来自多个本科专业：数学专业33人、生物科学专业71人、环境科学专业24人、物理专业17人，英语专业超84人。最后一类为<b>课堂笔记数据集</b>，我们共收集了近31名学生针对同一主题的课堂笔记。要求参与者完整记录讲座中讲师讲授的每一句话，在近10分钟的课程结束后，要求学生自行核对笔记并与同班同学比对，未对核对环节设置时间限制。相较于摘要类与学科类采集样本，课堂笔记样本中存在更多划改与修正痕迹。在四类采集任务中，我们未对参与者设置任何格式约束，例如字间距、用笔类型等，仅要求参与者若认为文本内容不当可自行划去。尽管多数参与者会在初次书写后自行修正，我们仍额外预留5分钟用于修正环节。

搜集汇总

背景与挑战

背景概述

该数据集是由500多名学生贡献的杂乱手写样本集合，包含摘要、论文、学科笔记和课堂笔记四类数据，模拟真实考试环境并包含大量修改痕迹。数据集以300dpi分辨率扫描，提供图像和对应转录文本，特别标注了划掉内容，适用于手写文本识别研究。