Multimed Tools Appl DOI 10.1007/s11042-017-4373-y
PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification Sk Md Obaidullah 1 & Chayan Halder 2 & K. C. Santosh 3 & Nibaran Das 4 & Kaushik Roy 2
Received: 10 April 2016 / Revised: 24 October 2016 / Accepted: 9 January 2017 # Springer Science+Business Media New York 2017
Abstract Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (PHDIndic_11), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada. PHDIndic_11 is composed of 1458 document text-pages written by 463 individuals from various parts of India. Further, we report the benchmark results for handwritten script identification (HSI). Beside script identification, the dataset can be effectively used in many other applications of document image analysis such as script sentence recognition/understanding, text-line segmentation, word segmentation/recognition, word spotting, handwritten and machine printed texts separation and writer identification.
* Kaushik Roy
[email protected] Sk Md Obaidullah
[email protected] Chayan Halder
[email protected] K. C. Santosh
[email protected] Nibaran Das
[email protected]
1
Department of Computer Science and Engineering, Aliah University, Kolkata, India
2
Department of Computer Science, West Bengal State University, Kolkata, India
3
Department of Computer Science, University of South Dakota, Vermillion, SD, USA
4
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
