PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts

Citation Author(s):
Obaidullah
Sk
Aliah University
Chayan
Halder
West Bengal State University
KC
Santosh
The University of South Dakota
Nibaran
Das
Jadavpur University
Kaushik
Roy
West Bengal State University
Submitted by:
Obaidullah Sk
Last updated:
Thu, 05/13/2021 - 08:59
DOI:
10.21227/p2p9-er60
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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.

Instructions: 

See the attached pdf in documentation for more details about the dataset and benchmark results. Cite the following paper if you use the dataset for research purpose.

Obaidullah, S.M., Halder, C., Santosh, K.C. et al. PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimed Tools Appl 77, 1643–1678 (2018). https://doi.org/10.1007/s11042-017-4373-y