MADCAT Phase 2 Training Set

Item Name: MADCAT Phase 2 Training Set
Author(s): David Lee, Safa Ismael, Dave Doermann, Stephanie Strassel, Song Chen, Stephen Grimes
LDC Catalog No.: LDC2013T09
ISBN: 1-58563-643-6
ISLRN: 828-846-182-243-2
Release Date: May 15, 2013
Member Year(s): 2013
DCMI Type(s): Text, StillImage
Data Source(s): weblogs, newswire, newsgroups
Project(s): MADCAT, OpenHaRT
Application(s): handwriting recognition, machine translation
Language(s): Arabic
Language ID(s): ara
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2013T09 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Lee, David, et al. MADCAT Phase 2 Training Set LDC2013T09. Web Download. Philadelphia: Linguistic Data Consortium, 2013.
Related Works: View


MADCAT (Multilingual Automatic Document Classification Analysis and Translation) Phase 2 Training Set contains all training data created by the Linguistic Data Consortium to support Phase 2 of the DARPA MADCAT Program. The data in this release consists of handwritten Arabic documents, scanned at high resolution and annotated for the physical coordinates of each line and token. Digital transcripts and English translations of each document are also provided, with the various content and annotation layers integrated in a single MADCAT XML output.

The goal of the MADCAT program is to automatically convert foreign text images into English transcripts. MADCAT Phase 2 data was collected from Arabic source documents in three genres: newswire, weblog and newsgroup text. Arabic speaking scribes copied documents by hand, following specific instructions on writing style (fast, normal, careful), writing implement (pen, pencil) and paper (lined, unlined). Prior to assignment, source documents were processed to optimize their appearance for the handwriting task, which resulted in some original source documents being broken into multiple pages for handwriting. Each resulting handwritten page was assigned to up to five independent scribes, using different writing conditions.

The handwritten, transcribed documents were checked for quality and completeness, then each page was scanned at a high resolution (600 dpi, greyscale) to create a digital version of the handwritten document. The scanned images were then annotated to indicate the physical coordinates of each line and token. Explicit reading order was also labeled, along with any errors produced by the scribes when copying the text.

The final step was to produce a unified data format that takes multiple data streams and generates a single MADCAT XML output file with all required information. The resulting madcat.xml file has these distinct components: (1) a text layer that consists of the source text, tokenization and sentence segmentation, (2) an image layer that consist of bounding boxes, (3) a scribe demographic layer that consists of scribe ID and partition (train/test) and (4) a document metadata layer.

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This release includes 27,814 annotation files in both GEDI XML and MADCAT XML formats (gedi.xml and madcat.xml) along with their corresponding scanned image files in TIFF format. The annotation results in GEDI XML output files include ground truth annotations and source transcripts.

Files are named as follows:

  • galeID_page#_scribeID.{tif|gedi.xml|madcat.xml}


Please view the following samples:


This work was supported in part by the Defense Advanced Research Projects Agency, MADCAT Program Grant No. HR0011-08-1-004 and GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.


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