Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

Devanagari Handwritten Character Dataset Data Set
Download: Data Folder, Data Set Description

Abstract: This is an image database of Handwritten Devanagari characters. There are 46 classes of characters with 2000 examples each. The dataset is split into training set(85%) and testing set(15%).

Data Set Characteristics:  

N/A

Number of Instances:

92000

Area:

Computer

Attribute Characteristics:

Integer

Number of Attributes:

N/A

Date Donated

2016-09-01

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

4315


Source:

The dataset was created by extraction and manual annotation of thousands of characters from handwritten documents.
Creator Name: Shailesh Acharya, Email: sailes437 '@' gmail.com, Institution: University of North Texas, Cell: +19402200157
Creator Name: Prashnna Kumar Gyawali, Email: gyawali.prasanna '@' gmail.com, Institution: Rochester Institute of Technology


Data Set Information:

Data Type: GrayScale Image
The image dataset can be used to benchmark classification algorithm for OCR systems. The highest accuracy obtained in the Test set is 98.47%. Model Description is available in the paper [Web Link]
More information on the dataset at [Web Link].


Attribute Information:

Image Format: .png
Resolution: 32 by 32
Actual character is centered within 28 by 28 pixel, padding of 2 pixel is added on all four sides of actual character.


Relevant Papers:

S. Acharya, A.K. Pant and P.K. Gyawali “Deep Learning Based Large Scale Handwritten Devanagari Character Recognition”,In Proceedings of the 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 121-126, 2015.



Citation Request:

The material maybe used for free with the following paper cited,
S. Acharya, A.K. Pant and P.K. Gyawali “Deep Learning Based Large Scale Handwritten Devanagari Character Recognition”,In Proceedings of the 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 121-126, 2015.


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML