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

2024-05-29 09:29:09
Miguel A. Ferrer, Abhijit Das, Moises Diaz, Aythami Morales, Cristina Carmona-Duarte, Umapada Pal


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.

Abstract (translated)

手写脚本识别在涉及多脚本和多语言环境中的应用中扮演着至关重要的角色。此外,它与人类认知有着深刻的联系。本文提供了一个新的数据库,用于对比手写识别算法,该数据库包含从各种脚本(如阿拉伯文、孟加拉文、古吉拉特文、印度教文、日语文、卡纳达文、孟加拉文、泰文等)的打印和手写文档。数据集包括来自当地报纸和手写信函以及不同母语的作者的笔记共计1135个文档。此外,这些文档被分为行和词,共计13,979行和86,655个词。 本文还提出了使用手工制作和深度学习方法轻松进行的基准。基准包括文档、行和词级别的手写和打印文档的结果。还提供了独立于文档/行/词级别和独立于打印/手写信函的手写识别结果。预计,这个多语言数据库将产生新的手写识别器,呈现各种挑战,并作为未来基于报告的识别研究的基础。



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