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Title: MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification
Authors: Ferrer Ballester, Miguel Ángel 
Das, Abhijit
Díaz Cabrera, Moisés 
Morales Moreno, Aythami 
Carmona Duarte, María Cristina 
Pal, Umapada
UNESCO Clasification: 120304 Inteligencia artificial
33 Ciencias tecnológicas
Keywords: Deep learning for script identification
Document analysis
Handcrafted features for script identification
Multi-lingual database
Multi-script database, et al
Issue Date: 2023
Project: Modelado cinemático 3D para la caracterización del movimiento humano, animal y robótico 
Journal: Cognitive Computation 
Abstract: Script identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspaper and handwritten letters as well as notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given. The new multi-lingual database is expected to create new script identifiers, present various challenges, including identifying handwritten and printed samples and serve as a foundation for future research in script identification based on the reported results of the three benchmarks.
ISSN: 1866-9956
DOI: 10.1007/s12559-023-10193-w
Source: Cognitive Computation [ISSN 1866-9956], (2023)
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