MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification

TL;DR

This paper introduces a comprehensive multi-lingual, multi-script database and benchmark for script identification, covering printed and handwritten documents across numerous scripts, facilitating future research and development in this field.

Contribution

It provides a new extensive dataset and benchmarks for script identification, including both printed and handwritten samples from diverse scripts, which was lacking in existing resources.

Findings

Benchmark results at document, line, and word levels

Performance analysis of handcrafted and deep learning methods

Identification of challenges in handwritten and printed script recognition

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.