Datasets per Publication

The collection contains simultaneously acquired online and offline samples. The online dataset is called "NFI-online" and was processed and segmented by Louis Vuurpijl. The offline dataset is called "NFI-offline" and was scanned by Vivian Blankers from the NFI. The offline signatures were automatically segmented and visually inspected by Louis Vuurpijl. Processing of the segmented signatures was performed by Katrin Franke. The collection contains:

The NLDCC-online signature collection contains in total 1953 online and 1953 offline signature files.

This is the dataset of the ICDAR 2013 - Gender Identification from Handwriting competition. If you use this database, please consider citing it as in [1].

This dataset is a subset of the QUWI dataset [2]. In sum, a total of 475 writers produced 4 handwritten documents: the first page contains an Arabic handwritten text which varies from one writer to another, the second page contains an Arabic handwritten text which is the same for all the writers, the third page contains an English handwritten text which varies from one writer to another and the fourth page contains an English handwritten text which is the same for all the writers.

Images have been acquired using an EPSON GT-S80 scanner, with a 300 DPI resolution. Images were provided in
JPG uncompressed format. The training set consists of the first 282 writers for which the genders are provided.
Participants were asked to predict the gender of the remaining 193 writers.

In addition to images, features extracted from the data were also provided in order to make the competition accessible by people without image processing skills. Those features are described in [3].

[1] Hassaïne, Abdelâali, et al. "ICDAR 2013 Competition on Gender Prediction from Handwriting." Document Analysis and Recognition (ICDAR), 2013 12th International Conference on. IEEE, 2013.

[2] Hassaïne, Abdelaali, et al. "The ICDAR2011 Arabic writer identification contest." Document Analysis and Recognition (ICDAR), 2011 International Conference on. IEEE, 2011.

[3] Al Maadeed, Somaya, and Abdelaali Hassaine. "Automatic prediction of age, gender, and nationality in offline handwriting." EURASIP Journal on Image and Video Processing 2014.1 (2014): 1-10.

SOURCE WEBSITES:

Statistics Canada  http://www.statcan.gc.ca/start-debut-eng.html

The World Bank,   http://www.worldbank.org/

Statistics Norway,    https://www.google.com/?gws_rd=ssl#q=Statistics+Norway

Statistics Finland, http://www.stat.fi/index_en.html

US Department of Justice,    https://www.justice.gov/

Geohive,   http://www.geohive.com/

US Energy Information Administration,    https://www.eia.gov/

US Census Bureau. http://www.census.gov/

 DATA COLLECTION:  About 1000 tables were collected from international statistical websited by DocLab graduate students in2009-2010. A perceptually random subset of 200 of these tables was converted (mostly from HTML) via EXCEL into CSV files and stored at DocLab. The original HTML files were not retained and their URLs were kept somewhat haphazardly. Many of these tables can still be found by a web search, but others have been modified, corrected, or updated on the original sites. The dataset now consists only of the 200 CSV file representation of 200 web tables.

GROUND TRUTH: The four critical cells (top-left and bottom right of the stub-header and top-left and bottom-right of the data region) were entered with an interactive tool (VeriClick) in 2011. Several GT errors were subsequently found during segmentation experiments and corrected. Researchers may still disagree on 2-3 tables on whether a particular row or column (unusual contents or units) belong to the data region.  The GT for all 200 tables is a single CSV file that allows adding to it and verifying the results of a header segmentation experiment.

AcTiV is the first publicly accessible annotated dataset designed to assess the performance of different Arabic VIDEO-OCR systems. The database has been named AcTiV for Arabic Text in Video. The challenges that are addressed by AcTiV-database are in text patterns variability and presence of complex background with various objects resembling text characters. AcTiV enables users to test their systems’ abilities to locate, track and read text objects in videos. The actual version of the dataset includes 80 videos collected from 4 different Arabic news channels. In the present work, two types of video stream were chosen: Standard-Definition (720x576, 25 fps) and High-Definition (1920x1080, 25fps).We mainly focus on text displayed as overlay in news video, which can be classified into two types: static text and dynamic one.

http://www.sage-eniso.org/content/fr/20/activ-data-base.html

Two sub-datasets are created from the AcTiV database: Activ-D (D for detection) and  Activ-R (R for recognition). AcTiV-D represents a sub-dataset of nonredundant frames used to measure the performance of single-frame based methods to detect/localize text regions in still HD/SD images. AcTiV-R is a sub-dataset of cropped images used to measure the performance of Arabic OCR systems to read texts in video frames.

Smartphones are replacing personal scanners. They are portable, connected, powerful and affordable. They are on their way to become the new entry point in business processing applications like document archival, ID scanning, check digitization, just to name a few. In order keep our workflows streamlined, we need to make those new capture device as reliable as batch scanners.

We believe an efficient capture process should be able to:

This competition is focused on the first step of this process: efficiently detect and segment document regions, as illustrated by following video showing the ideal output for the preview phase of some acquisition session.

For this challenge, the input will be a set of videoclips containing a document from a predefined set, and the output should be an xml file containing the quadrilateral coordinates in which we can find the document per each frame of the video.

The goal of the competition is to extract the textual content from document images which are captured by mobile phones. The images are taken under varying conditions to provide a challenging input. Please refer to The Dataset page for more detail information.

The sample dataset contains 3630 mobile-phone-captured images from 20 documents, along with their ground truth. The test dataset contains 8470 images.

odern smartphones have a revolutionary impact on the way people digitize the paper documents. The wide ownership of smartphones and their ease of use for digitizing paper documents has resulted into massive amount of imagery data of digitized paper documents. The goal of digitizing the paper documents is not only to archive them for sharing but also, most of the times, to process them by automated document image processing systems. The latter extracts the content of the document images for recognizing it, indexing it, verifying it, comparing it with a database etc. However, it is a known fact that the cameras of the smartphones are optimized for capturing natural scene images. Taking a simple photo of a paper document does not ensure that its content would be exploitable by automated document image processing systems. This could happen because of the light conditions, the resolution of the image, the camera noise, the perspective distortion, the physical distortions (folds etc.) of the paper, the out-of-focus blur and/or the motion blur during capture. To ensure that the content of a captured document image is exploitable by automated systems, it is important to automatically assess the quality of a captured document image in real-time. Otherwise most of the times it is not possible to re-capture the document image later on, because the original document is not available anymore. Assessing the quality of a captured document image is also required in situations where the captured document images are to-be transmitted for further processing.

The quality assessment step is an important part of both the acquisition and the digitization processes. Assessing document quality could aid users during the capture process or help improve image enhancement methods after a document has been captured. Current state-of-the-art works lack databases in the field of document image quality assessment. 

In order to provide a baseline benchmark for quality assessment methods for mobile captured documents, we present a database for quality assessment that contains both single- and multiply-distorted document images.

The Bentham collection consists of a set of images of a collection of works on law and moral philosophy written by the philosopher Jeremy Bentham.

The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant varibilities and difficulties regarding the quality of text images and writting styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts.

This dataset is free available for research purposes and it is provided into two parts: the images and the GT. The GT includes information about the  layout and the transcription at line level of each image in PAGE format. 

Keywords

Forensic analysis, signatures, handwriting, off-line, on-line, verification, identification, evaluation, likelihood ratios

Description

This site provides an assessment of the quality-time performance of binarization algorithms.

Over 5.5M synthetic, 708 photographed  and 116  "real-world" documents are used in such assessment. The results of binarization are shown in statistical tables together with time elapsed. The binary images may be obtained on-demand. The current version of the platforms presents the results of 20 "classical" global binarization algorithms. The platform is being extended to include more recent algorithms.

The collection contains offline signature samples. The signatures were collected under the supervision of Bryan Found and Doug Rogers in the years 2002 and 2006, respectively. The images were scanned at 600dpi resolution and cropped at the Netherlands Forensic Institute for the purpose of 4NSigComp2010 signature verification competition.

The dataset provide more than 11 000 expressions handwritten by hundreds of writers from different countries, merging the data sets from 4 CROHME competitions. Writers were asked to copy printed expressions from a corpus of expressions. The corpus has been designed to cover the diversity proposed by the different tasks and chosen from an existing math corpus and from expressions embedded in Wikipedia pages. Different devices have been used (different digital pen technologies, white-board input device, tablet with sensible screen), thus different scales and resolutions are used. The dataset provides only the on-line signal.

In the last competition CROHME 2013 the test part is completely original and the train part is using 5 existing data sets:

In CROHME 2014 a new test set has been created with 987 new expressions and 2 new tasks has been added: isolated symbol recognition and matrix recognition. Train and test files as the evaluation scripts for these new tasks are provided. For the isolated symbol datasets, elements are extracted from full expression using the existing datasets, which also includes segmentation errors. For the matrix recognition task, 380 new expressions have been labelled and split into training and test sets.

Furthermore, 6 participants of the 2012 competition provide their recognized expressions for the 2012 test part. This data allows research on decision fusion or evaluation metrics...

The Bentham collection consists of a set of images of a collection of works on law and moral philosophy written by the philosopher Jeremy Bentham.

The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant varibilities and difficulties regarding the quality of text images and writting styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts.

This dataset is free available for research purposes and it is provided into two parts: the images and the GT. The GT includes information about the  layout and the transcription at line level of each image in PAGE format. 

DIVA-HisDB is a collection of three medieval manuscripts that have been chosen regarding the complexity of their layout [1], together with partners from e-codices (http://www.e-codices.unifr.ch/) and the Humanities faculty in the University of Fribourg:

Preview pages below:

HANDS-VNOnDB (VNOnDB in short) provides 1,146 Vietnamese paragraphs of handwritten text composed of 7,296 lines, more than 480,000 strokes and more that 380,000 characters written by 200 Vietnamese. Writers were asked to write freely ground-truth text from a corpus of Vietnamese text. Our ground-truth text is derived from the VieTreeBank corpus which contains all of Vietnamese characters and some special symbols since it bases on Vietnamese newspapers. For collecting patterns, we used Fujistu PC Tablets (FMVT8170) with stylus pen at high sampling rate (120Hz). Each sequence contains multiple lines within various delayed strokes. VNOnDB is available for research purpose only. For commercial purposes and sell products, please contact us via email.

This is the dataset used in the Handwritten Text Recognition (HTR) contest on the READ dataset  that was held in the context of the International Conference on Frontiers in Handwriting Recognition 2016. This contest aimed to bring together researchers working on off-line HTR and provide them a suitable benchmark to compare their techniques on the task of transcribing typical historical handwritten documents. Two tracks with different conditions on the use of training data were proposed. The handwritten images for this contest were drawn from the German document Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805, used in the READ project. The selected dataset has been written by several hands and entails significant variabilities and difficulties.

The dataset provides more than 12 000 expressions handwritten by hundreds of writers from different countries, merging the data sets from 4 previous CROHME competitions and adding new ressources. Writers were asked to copy printed expressions from a corpus of expressions. The corpus has been designed to cover the diversity proposed by the different tasks and chosen from an existing math corpus and from expressions embedded in Wikipedia pages. Different devices have been used (different digital pen technologies, white-board input device, tablet with sensible screen), thus different scales and resolutions are used. The dataset provides only the on-line signal.

The training and validation sets are using CROHME 2014 datasets, but 1147 new on-line handwritten expressions are used in the CROHME 2016 test dataset. Furthemore, this new package provide updated version of tools and new math ressources: more than 500K of LaTeX expressions from wikipedia to train language models.

All these ressources allow to adress 4 different tasks: Formulas recognition, Structure recognition, isolated Symbol recognition and Matrices recognition.

HANDS-VNOnDB2018 (VNOnDB2018 in short [1]) is used for ICFHR2018 Competition on Vietnamese Online Handwritten Text Recognition using VNOnDB. It provides 1,146 Vietnamese paragraphs of handwritten text composed of 7,296 lines, more than 480,000 strokes and more that 380,000 characters written by 200 Vietnamese.

Writers were asked to write freely ground-truth text from a corpus of Vietnamese text. Our ground-truth text is derived from the VieTreeBank corpus which contains all of Vietnamese characters and some special symbols since it bases on Vietnamese newspapers. For collecting patterns, we used Fujistu PC Tablets (FMVT8170) with stylus pen at high sampling rate (120Hz). Each sequence contains multiple lines within various delayed strokes. VNOnDB2018 is available for research purpose only. For commercial purposes and sell products, please contact us via email.

The dataset contains 40 images for training and validation and 10 images for testing.

The document pages in the dataset are from multiple sources which are digitized using different devices. This increased variance makes the dataset especially challenging for segmentation task.