H1 Children's Writing
|Item Name:||H1 Children's Writing|
|LDC Catalog No.:||LDC2016T01|
|Release Date:||April 18, 2016|
|DCMI Type(s):||StillImage, Text|
|Application(s):||handwriting recognition, machine translation|
H1 Children’s Writing Agreement
|Online Documentation:||LDC2016T01 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Berkling, Kay. H1 Children's Writing LDC2016T01. Web Download. Philadelphia: Linguistic Data Consortium, 2016.|
H1 Children's Writing was developed by the Cooperative State University Baden-Württemberg, University of Education. It consists of 996 texts written over three months by 88 German school children age seven through eleven years.
The data in this corpus was collected by an elementary school in Baden Württemberg, Germany and digitized at the Cooperative State University during the second half of the 2014/2015 school year. Three second and third grade classrooms participated in the collection.
Texts were written within regular class settings. The students were presented with a picture and were asked to write a story, to describe the picture or if unable to write a text, to list what they saw in the picture. The pictures were designed to enhance the output with respect to important spelling error categories, namely, the marking of short vowels with a silent consonant letter and the correct spelling of the long vowel. The children were allowed at least 15 minutes to write the texts. This exercise was repeated weekly for 12 weeks.
LDC has also released H2, E2, ERK1 Children's Writing (LDC2018T05).
Most of the participants were multilingual. Out of 85 children for whom metadata is available, 57 students were multilingual speakers and 28 students were monolingual German speakers. The following metadata is included for each text in the database: school week of collection; school type (always elementary school); age; gender; grade/classroom; language spoken at home; and school materials used for German (Jojo).
In all, 996 texts representing 62,764 tokens were collected. The texts were digitized in two forms: (1) the original text, including all errors (achieved), and (2) the intended (target) text, where all spelling errors were removed. Annotations were added to both the achieved text and the target text to distinguish words that should not be analyzed for spelling errors, such as names or foreign words. For sentence-level analysis, syntax errors were annotated by marking substitutions, deletions and insertions at the word level. In such cases, the used word was analyzed for spelling, and the correct word was used for sentence structure analysis.
Original handwriting is presented as pdf documents and the converted text as UTF-8 plain text in csv documents.
Please view the following samples:
None at this time.