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Supplementary material 1 from: Madan C (2016) Improved understanding of brain morphology through 3D printing: A brief guide. Research Ideas and Outcomes 2: e10266. https://doi.org/10.3897/rio.2.e10266
Surface meshes (.ply) from FreeSurfer subject 'bert'.
Supplementary material 1 from: Madan C (2016) Improved understanding of brain morphology through 3D printing: A brief guide. Research Ideas and Outcomes 2: e10398. https://doi.org/10.3897/rio.2.e10398
Surface meshes (.ply) from FreeSurfer subject 'bert'.
Figure 1 from: Madan C (2016) Improved understanding of brain morphology through 3D printing: A brief guide. Research Ideas and Outcomes 2: e10398. https://doi.org/10.3897/rio.2.e10398
Figure 1 - Illustration of the processing pipeline involved in converting a structural MRI volume to a print-ready surface mesh.
Figure 2 from: Madan C (2016) Improved understanding of brain morphology through 3D printing: A brief guide. Research Ideas and Outcomes 2: e10398. https://doi.org/10.3897/rio.2.e10398
Figure 2 - Photos of a resulting 3D-printed surface. (A) View of the full models. (B) Close-up of the coronal cross-section. (C) Close-up of the lateral surface. (D) Photo of the Machina Mk2 X20 printers used to print the models, located at the University of Alberta Libraries.
Figure 2 from: Madan C (2016) Improved understanding of brain morphology through 3D printing: A brief guide. Research Ideas and Outcomes 2: e10266. https://doi.org/10.3897/rio.2.e10266
Figure 2 - Photos of a resulting 3D-printed surface. (A) View of the full models. (B) Close-up of the coronal cross-section. (C) Close-up of the lateral surface. (D) Photo of the Machina Mk2 X20 printers used to print the models, located at the University of Alberta Libraries.
Figure 1 from: Madan C (2016) Improved understanding of brain morphology through 3D printing: A brief guide. Research Ideas and Outcomes 2: e10266. https://doi.org/10.3897/rio.2.e10266
Figure 1 - Illustration of the processing pipeline involved in converting a structural MRI volume to a print-ready surface mesh.
Raw data for 'Light-Activated 3D Printed Fish-Like Actuator'
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Open data of printed cells
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Laser printed cells, Optical microscopy image
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The preparation of the COgITOR Soft Robot soccer ball shaped skin with SOLIDWORKS at the PC for 3D printing
<p><a href="https://www.cogitor-project.eu/">Homepage » COGiTOR (cogitor-project.eu)</a></p> <p>This video was generated 3 November 2023 by Swiss3D GmbH.</p> <p> </p> <p> </p>
M-POPP datasets: Datasets for full page text recognition and information extraction from French handwritten and printed marriage records
<h1><strong>M-POPP datasets</strong></h1> <p>This repository contains 2 datasets created within the <strong>EXO-POPP project</strong> (<a href="https://exopopp.hypotheses.org/">Optical EXtraction of handwritten named entities for marriage records of the POPulation of Paris</a>) for the task of text recognition and information extraction. These datasets have been published in <a href="https://arxiv.org/abs/2404.19329"><code>End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940</code></a> <code>[1]</code>at ICDAR 2024.</p> <p><strong>This version contains the labels for Handwritten Text Recognition and Handwritten Text Recognition + Information Extraction as used in our new paper "<a href="https://hal.science/hal-04555188">DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents</a>" [3].</strong></p> <p><strong>This version makes corrections to the handwritten dataset. </strong>More precisely, it corrects a few errors in transcription annotations and named entities.</p> <p><strong>The printed dataset is unchanged compared to version 2.</strong></p> <p><strong>The performances of the models described in [1] and [3] are detailled in the Leaderboard section.</strong></p> <h2><strong>General information</strong></h2> <p>The <strong>EXO-POPP project</strong> aims to establish a comprehensive database comprising 300,000 marriage records from Paris and its suburbs, spanning the years 1880 to 1940, which are preserved in over 130,000 scans of double pages. Each marriage record may encompass up to 118 distinct types of information that require extraction from plain text. The M-POPP corpus (which stands for Marriage records of the POPulation of Paris) is the corpus on which the EXO-POPP project focuses. This corpus was built by gathering the marriage records of Paris and its suburb regions (Hauts- de-Seine, Seine-Saint-Denis, Val-de-Marne).</p> <p>The M-POPP corpus are a subset of the M-POPP database with annotations for full-page text recognition and named entity recognition/information extraction from both handwritten and printed documents. The first dataset comprises handwritten marriage records, while the second dataset consists of typewritten marriage records. It should be noted that even in typewritten marriage records, some handwritten information occurs, especially concerning the names of the spouses, and notes in the margin.<br>The dataset contains single-page images obtained from the original scans of double pages via page segmentation.</p> <p>The structure of the files is the following:</p> <ul> <li>handwritten: <em>the handwritten dataset</em><br> <ul> <li>images: <em>images of the dataset divided following the split used in [1]</em><br> <ul> <li>train</li> <li>valid</li> <li>test</li> </ul> </li> <li>labels: <em>labels for joint handwritten text recognition and information extraction for each encoding tested in [1]</em></li> </ul> </li> <li>printed: <em>the printed dataset</em><br> <ul> <li>images: <em>images of the dataset divided following the split used in [1]</em><br> <ul> <li>train</li> <li>valid</li> <li>test</li> </ul> </li> <li>labels: <em>labels for joint handwritten text recognition and information extraction for each encoding tested in [1]</em></li> </ul> </li> <li>encoding-2-to-encoding-5.json: <em>a JSON file giving the correspondence between the symbols of encoding 2 and encoding 5.</em></li> </ul> <p> </p> <p>Table 1: Details on the split of the handwritten dataset.</p> <table> <tbody> <tr> <td> </td> <td>Train</td> <td>Validation</td> <td>Test</td> </tr> <tr> <td>Pages</td> <td>250</td> <td>32</td> <td>32</td> </tr> <tr> <td>Acts</td> <td>344</td> <td>51</td> <td>53</td> </tr> <tr> <td>Named entities</td> <td>16727</td> <td>2223</td> <td>2517</td> </tr> </tbody> </table> <p> </p> <p>Table 2: Details on the split of the printed dataset.</p> <table> <tbody> <tr> <td> </td> <td>Train</td> <td>Validation</td> <td>Test</td> </tr> <tr> <td>Pages</td> <td>116</td> <td>14</td> <td>13</td> </tr> <tr> <td>Acts</td> <td>363</td> <td>43</td> <td>30</td> </tr> <tr> <td>Named entities</td> <td>22036</td> <td>2559</td> <td>2405</td> </tr> </tbody> </table> <p> </p> <p>Table 3: Average annotation statistics per act for the two M-POPP datasets.</p> <table> <tbody> <tr> <td>Dataset</td> <td># of characters</td> <td># of words</td> <td># of named entities</td> </tr> <tr> <td>Handwritten</td> <td>1519</td> <td>231</td> <td>48</td> </tr> <tr> <td>Printed</td> <td>1328</td> <td>200</td> <td>60</td> </tr> </tbody> </table> <p> </p> <h3><strong>Document structure Annotation</strong></h3> <p>We employ the procedure applied in [2], which involves adding opening and closing tags to the character set for each text block we want to recognize.<br>In total, we define four types of text blocks.</p> <ul> <li>Block A is located in the margin and contains the last names of the married couple, possibly with their first names and the date of the marriage.</li> <li>Block B is the body of the text. Block B is the one that contains most of the information to be extracted.</li> <li>Block C is optional and corresponds to marginal notes used in various cases, such as the mention of a divorce or a correction made to the act.</li> <li>Block D corresponds to a set containing a block A and a block B, optionally with one or more blocks C.</li> </ul> <p> </p> <h3><strong>Information Extraction annotation</strong></h3> <p>The dataset contains 118 information categories. As explained in the paper, we broke down the named entities into sub-elements pertaining to 4 hierarchical levels, which reduces the total number of categories to 23 instead of 118. Notice that level 1, 2, and 3 categories do not encode named entities but rather the relations that may occur between some lower level categories for example: (day, birth, husband) encodes the fact that the annotated piece of text is the date of birth of the husband. </p> <p>For these datasets, we chose to represent these hierarchical elements with emojis. For instance, the information <em>first name</em> is represented by the emoji 💬.<br>The meaning of each emoji can be found in Table 4. To determine the best way to encode named entities in the ground truth, we compared in [1] 5 types of encoding. To illustrate these encodings, let’s take for instance <em>Louis Alexandre MOUDEL</em> that we define as the father of the bride, where <em>Louis Alexandre</em> are his two first names, and <em>Moudel</em> is his last name. </p> <p>1) Single separate tags before each word: In this approach, each level of information is indicated by a dedicated tag, and the tags are placed before the word they encode information for. With this encoding, the ground truth for the example would be:</p> <p>💬👴👰Louis 💬👴👰Alexandre 🗨️👴👰MOUDEL</p> <p>2) Single separate tags after each word: Similar to the previous approach, except here the tags are placed after the word. With this encoding the previous example becomes:</p> <p>Louis👰👴💬 Alexandre👰👴💬 MOUDEL👰👴🗨️</p> <p>3) Open & close separate tags: Here, each word presenting information to be extracted is surrounded by one or more opening and closing tags, where each tag encodes a level of information. So the example would be as:</p> <p><👰> <👴> <💬> Louis <\💬> <\👴> <\👰><br><👰> <👴> <💬> Alexandre <\💬> <\👴> <\👰><br><👰> <👴> <🗨️> MOUDEL <\🗨️> <\👴> <\👰></p> <p>4) Nested open & close separate tags: Similar to the previous approach, but this time a tag is closed only when the encoded information is no longer the same for that level of information. We can see in the example below that the tags for wife and father are only used twice.</p> <p><👰> <👴> <💬> Louis Alexandre <\💬> <🗨️> MOUDEL <\🗨️></p> <p>5) Single combined tags after each word: In the last approach, one tag encodes all the hierarchical levels constituting information. The tags are located after the word they encode information for. </p> <p>Louis<wife_father_first_name> Alexandre<wife_father_first_name> MOUDEL<wife_father_family_name></p> <p>NB: In the labels file of encoding 5, the information are still encoded with emojis but the chosen emojis do not have a semantic meaning due to the number of information categories to be represented. The correspondence between the symbols of encoding 2 and encoding 5 can be found in the file <em>encoding-2-to-encoding-5.json</em>.<em><br></em></p> <p> </p> <p>Table 4: Details of the hierarchical breakdown of named entities. Each tag is placed in the corresponding hierarchical level and associated with the emoji representing it.</p> <table> <tbody> <tr> <td>Level</td> <td>Tags</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>1</td> <td>Administrative 📖</td> <td> <pre>Husband<code> 👨</code></pre> </td> <td>Wife 👰</td> <td>Witness 🥸</td> </tr> <tr> <td>2</td> <td>Father 👴</td> <td>Mother 👵</td> <td>Ex-husband 💔</td> <td> </td> </tr> <tr> <td>3</td> <td>Birth 🏥</td> <td>Residence 🏠</td> <td> </td> <td> </td> </tr> <tr> <td>4</td> <td>First name 💬</td> <td>Family name 🗨️</td> <td>Age ⌛</td> <td>Occupation 🔧</td> </tr> <tr> <td>5</td> <td>Street number 🔟</td> <td>Street type 🛣</td> <td>Street name 🔠</td> <td>City 🌆</td> </tr> <tr> <td> </td> <td>Department 🗺</td> <td>Country 🗺</td> <td>Day 🌞</td> <td>Month 📅</td> </tr> <tr> <td> </td> <td>Year 🗓</td> <td>Hour ⏰</td> <td>Minute ⏱</td> <td> </td> </tr> </tbody> </table> <p> </p> <h2><strong>Leaderboard</strong></h2> <h3><strong>Results on M-POPP handwritten</strong></h3> <p><strong>HTR<br></strong></p> <p>The following table contains the current leaderboard of M-POPP v3 for HTR on the handwritten dataset.</p> <p>In this configuration, layout block C is not considered.</p> <p>These results for DAN NER are given using the named entity encoding format 5 described above.</p> <p>HTR stands for Handwritten Text Recognition and HTR+IE for combined Handwritten Text Recognition and Information Extraction.</p> <p>Metrics are expressed in percentages.</p> <table> <tbody> <tr> <td>Method</td> <td>CER</td> <td>WER</td> <td>LOER</td> <td>mAP CER</td> </tr> <tr> <td>DAN - HTR [1]</td> <td>7.21</td> <td>16.42</td> <td>5.35</td> <td>83.03</td> </tr> <tr> <td>DAN NER - HTR + IE [1]</td> <td>6.52</td> <td>14.80</td> <td>3.79</td> <td>86.29</td> </tr> <tr> <td>DANIEL - HTR [3]</td> <td>5.72</td> <td>14.08</td> <td>1.34</td> <td>89.28</td> </tr> </tbody> </table> <p> </p> <p><strong>NER</strong></p> <p>The following table contains the current leaderboard of M-POPP v3 for NER on the handwritten dataset.</p> <p>In this configuration, layout block C is not considered.</p> <p>These results are given using the named entity encoding format 5 described above.</p> <p>Metrics are expressed in percentages.</p> <table> <tbody> <tr> <td>Method</td> <td>F1</td> </tr> <tr> <td>DAN NER [1]</td> <td>76.37</td> </tr> <tr> <td>DANIEL [3]</td> <td>76.37</td> </tr> </tbody> </table> <h3> </h3> <h3><strong>Results on M-POPP printed</strong></h3> <p><strong>HTR</strong></p> <p>The following table contains the current leaderboard of this version for TR on the printed dataset.</p> <p>The labels, and therefore the results, are the same as in version 2.</p> <p>In this configuration, only layout block B is considered.</p> <p>These results for DAN NER are given using the named entity encoding format 5 described above.</p> <p>TR stands for Text Recognition and TR+IE for combined Text Recognition and Information Extraction.</p> <p>Metrics are expressed in percentages.</p> <p><strong>NB:</strong> The model DANIEL from [3] was not evaluated on the printed dataset.</p> <table> <tbody> <tr> <td>Method</td> <td>CER</td> <td>WER</td> </tr> <tr> <td>DAN - TR [1]</td> <td>0.88</td> <td>3.17</td> </tr> <tr> <td>DAN NER - TR + IE [1]</td> <td>1.54</td> <td>3.55</td> </tr> </tbody> </table> <p> </p> <p><strong>NER</strong></p> <p>The following table contains the current leaderboard of this version for NER on the printed dataset.</p> <p>The labels, and therefore the results, are the same as in version 2.</p> <p>In this configuration, only layout block B is considered.</p> <p>These results are given using the named entity encoding format 5 described above.</p> <p>Metrics are expressed in percentages.</p> <p><strong>NB:</strong> The model DANIEL from [3] was not evaluated on the printed dataset.</p> <table> <tbody> <tr> <td>Method</td> <td>F1</td> </tr> <tr> <td>DAN NER [1]</td> <td>93.04</td> </tr> </tbody> </table> <h2> </h2> <h2><strong>Citation Request</strong></h2> <p>If you publish material based on this database, we request you to include a reference to the paper <code><a href="https://arxiv.org/abs/2404.19329">T. Constum, L. Preel, T. Paquet, P. Tranouez, S. Brée, End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940, International Conference on Document Analysis and Recognition (ICDAR), Athens, Greece, 2024</a>.</code></p> <p> </p> <h2><strong>Bibliography</strong></h2> <p><a href="https://arxiv.org/abs/2404.19329">1: T. Constum, L. Preel, T. Paquet, P. Tranouez, S. Brée: End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940, International Conference on Document Analysis and Recognition (ICDAR), Athens, Greece, 2024.</a></p> <p>2: D.Coquenet, C. Chatelain, T. Paquet: DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence pp. 1–17 (2023).</p> <p><a href="https://hal.science/hal-04555188/">3: T. Constum, T. Paquet, P. Tranouez: DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents, preprint, 2024 </a></p>
Figure 2 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416
Figure 2 Fluid material scanning box: A dimensions of top-down and cross-section view of box plans, and finished box with glass affixed in B top and C bottom view.
Figure 3 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416
Figure 3 Scanning process images: A scanning process with scan view setup, box covers, full scan, and cropped image B required fill levels of fluid for box covers to prevent bubbles and standoffs (left) to prevent an uneven scan (right) C six boxes set up on scanner; and D resulting full scan. Clear Plexiglas cube standoffs B and slip-joint rings D (lower-right) improve scans.
Figure 4 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416
Figure 4 Cropped scan and scan resolution comparison: A final cropped scan of EMEC 37259, Psychoglyphaormiae (Ross, 1938) ♂, with standard layout of labels and specimens B–D comparison of scan resolution settings of 600 dpi, 1200 dpi, and 2400 dpi to show 100% scale display quality differences. Black scale bar is 5 mm for Rhyacophilaharmstoni Ross, 1944 ♂ (EMEC 41255, NV: White Pine Co.) in all images. Images are unmodified in software (e.g., no sharpening, white balance, or color correction).
Figure 1 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416
Figure 1 Fluid-stored arthropods in A a museum jar with shell vials B shell vials in a jar rack C racks in storage shelves at the Essig Museum of Entomology. Other fluid-based storage approaches include D screw-top scintillation vials and stoppered vials. Individually capped vials are commonly stored in racks on shelves.
Figure 5 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416
Figure 5 Example scans of different species and life stages of Essig Museum of Entomology Trichoptera showing detail of morphological features ADolophilodesnovusamericana (Ling, 1938) ♂, CA: Marin Co., EMEC 373433 BWormaldia sp. larvae, CA: Nevada Co., EMEC 1194742 CLimnephilusfrijole Ross, 1944 genitalic dissections of ♀♂, CA: Modoc Co., EMEC 373223 D larva and E case of Yphriacalifornica (Banks, 1907), CA: El Dorado Co., EMEC 373355 FLimnephilidae pupa, no location data, EMEC 373316 GPsychoglyphaormiae ♀ (Ross, 1938), CA: Nevada Co., EMEC 373259 HPsychoglypha sp. ♀, CA: Nevada Co., EMEC 373266 and IHesperophylaxdesignatus (Walker, 1852) case, CA: Mono Co., EMEC 373246. All scans are unmodified in software (no sharpening, white balance, or color correction). Scale bar: 5mm.
a) Structure of the printed circuit board (b) Equivalent circuit of the device.
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Utilization of 3D Scanning and Printing Techniques in Making External Upper Extremity Prostheses: A Systematic Literature Review
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3D-printed gelled electrolytes for electroanalytical applications
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Data repository for Uniaxial compression of 3D printed samples with voids: laboratory measurements compared with Effective Medium Theory
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