{ "fetched_at": "2026-10-19T08:25:26Z", "source_url": "https://www.kaggle.com/competitions/WIC2011/data", "pages": [ { "name": "Custom", "content": "The dataset used for the competition is a sub-set of a large dataset that has been collected in Qatar University thanks to several volunteers with different backgrounds. The collection of data has been done under the supervision of Dr. Somaya Al-Maadeed.

The dataset has been digitized by the research assistant Wael Al-Ayoubi using an HP officejet 6500A Plus scanner. It has been cropped and preprocessed by Dr. Abdelâali Hassaïne.

Using this dataset is free of charge for academic purposes (please mention the competition article that will be published in ICDAR2011 to cite this dataset). For industrials purposes, please contact Dr. Somaya Al-Maadeed.
This competition is supervised by all of Dr. Somaya Al-Maadeed, Prof. Jihad Mohamad Alja’am, Prof. Ali Mohamed Jaoua from Qatar University and Prof. Ahmed Bouridane from Northumbria University.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "In this contest, more than 50 writers were asked to write three different paragraphs in Arabic language. The first two paragraphs are used for training and the third one for testing. (For some writers, the first two paragraphs have been removed from the training set to test the ability of systems to detect unknown writers.)

Images are provided in PNG color, gray and binary format. The binarization has been performed using Otsu's method. Each folder contains two subfolders “train” and “test”. The folder “train” contains images having the following format XXX_Y.png where XXX represents the ID of the writer and Y represents the number of the paragraph.
The folder “test” contains images of the third paragraph, they all have the following format ZZ.png, where ZZ is a randomly generated string.

Participants are asked to provide the probability that a ZZ images corresponds to a certain XXX writer. Also, like mentioned before, some ZZ images do not actually correspond to any writer in the training set. Participants are asked to provide the probability that the writer is unknown.

The competition is judged using the mean absolute error evaluation metrics. 

For participants who are not familiar with image processing, more than 70 features are provided. Some of these features are real values, others are histograms. The list below shows the name and the number of values in each of these features.
  • NumberOfConnectedComponents_1 (1 value)
  • NumberOfHoles_1 (1 value)
  • Spatial, central, normalized central moments and Hu moments(values)
  • XProjectionHist10 (histogram of 10 values)
  • YProjectionHist10 (histogram of 10 values)
  • XFilledProjectionHist10 (histogram of 10 values)
  • YFilledProjectionHist10 (histogram of 10 values)
  • Distribution10x10_100 (histogram of 100 values)
  • Barycenter2 (histogram of 2 values)
  • numberOfPixels1 (1 value)
  • Fourier_1 (1 value)
  • Fourier_5 (histogram of 5 values)
  • Fourier_9 (histogram of 9 values)
  • Fourier_15 (histogram of 15 values)
  • nbbranches_1 (1 value)
  • LengthsOfBranchesHist_10 (histogram of 10 values)
  • ThicknessLengthsCircleHist30 (histogram of 30 values)
  • tortuosityHist10 (histogram of 10 values)
  • tortuosityDirectionHist10 (histogram of 10 values)
  • tortuosityDerivateHist10 (histogram of 10 values)
  • tortuosityDerivateDirectionHist10 (histogram of 10 values)
  • DirectionPerpendicular5Hist10 (histogram of 10 values)
  • CurvaturePerpendicular5Hist100 (histogram of 100 values)
  • luminanceHist256 (histogram of 256 values)
  • CurvatureAli5Hist100 (histogram of 100 values)
  • CurvaturesDerivateAli5Hist100 (histogram of 100 values)
  • CurvatureAli10Hist100 (histogram of 100 values)
  • CurvaturesDerivateAli10Hist100(histogram of 100 values)
  • CurvatureAli15Hist100(histogram of 100 values)
  • CurvaturesDerivateAli15Hist100(histogram of 100 values)
  • CurvatureAli20Hist100(histogram of 100 values)
  • CurvaturesDerivateAli20Hist100(histogram of 100 values)
  • chaincodeHist_4(histogram of 4 values)
  • chaincodeHist_8(histogram of 8 values)
  • chaincode8order2_64(histogram of 64 values)
  • chaincode4order2_16(histogram of 16 values)
  • chaincode4order3_64(histogram of 64 values)
  • chaincode8order3_512(histogram of 512 values)
  • chaincode4order4_256(histogram of 256 values)
  • chaincode8order4_4096(histogram of 4096 values)
We are particularly interested in methods that uses only those features but usage of other features is most welcome.
The submissions must be in the following format: The first line contains ranked XXX names and then “unknown”. First column contains ranked ZZ images. Each cell contains the probability that a ZZ images corresponds to a certain XXX writer. Cells of the last column contain the probability that a ZZ image corresponds to an unknown writer. The sum of probabilities does not have to be 1, neither by line, nor by column and not even for the whole file! Please see sample_entry.csv for an example.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "
Writer identification is important for forensic analysis, helping experts to deliberate on the authenticity of documents. This competition aims to further the science of writer identification. It requires participants develop algorithms that can identify handwriting. This is a difficult problem because a writer never reproduces exactly the same characters. 

Writer identification generally requires two steps. The first is an image-processing step, where features are extracted from the images. The second step is a classification step, where the document is assigned to the “closest” document in the dataset according to the “difference” between their features.

In this contest, a previously unpublished data has been made available, containing the writings of more than 50 writers. Participants are asked to provide a similarity score, showing how probable it is that two documents are written by the same person. For participants who are not familiar with image-processing, a set of geometrical features extracted have been provided.

This contest is organized in conjunction with the International Conference on Document Analysis and Recognition (ICDAR2011). The winner of this contest will win $1,000 and will be acknowledged in a special session at the ICDAR2011, which will be held in Beijng in September 18-21. The prize money is paid on condition that the winning entrant shares their methodology. ", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Rules", "content": "\r\n\t\r\n

One account per participant

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You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.

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No private sharing outside teams

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\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n

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Team Mergers

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\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n

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Team Limits

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There is no maximum team size.

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Submission Limits

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You may submit a maximum of 1 entries per day.

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You may select up to 1 final submissions for judging.

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Competition Timeline

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Start Date: 2/28/2011 12:00 AM UTC

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Merger Deadline: None

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Entry Deadline: None

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End Date: 4/10/2011 12:00 PM UTC

\r\n\t\r\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }