Performance of Binarization Algorithms on Tamizhi Inscription Images: An Analysis

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-04-08 DOI:10.1145/3656583

Monisha Munivel, V S Felix Enigo

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Abstract

Binarization of Tamizhi (Tamil-Brahmi) inscription images are highly challenging as it is captured from very old stone inscriptions that exists around 3rd century BCE in India. The difficulty is due to the degradation of these inscriptions by environmental factors and human negligence over ages. Though many works have been carried out in the binarization of inscription images, very few research was performed for inscription images and no work has been reported for binarization of inscriptions inscribed on irregular medium. The findings of the analysis hold true to all writings that are carved in irregular background. This paper reviews the performance of various binarization techniques on Tamizhi inscription images. Since no previous work was performed, we have applied the existing binarization algorithms on Tamizhi inscription images and analyzed the performance of these algorithms with proper reasoning. In future, we believe that this reasoning on the results will help a new researcher, to adapt or combine or devise new binarization techniques.

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Tamizhi 铭文图像的二值化算法性能：分析

Tamizhi（泰米尔-婆罗米）碑文图像的二值化具有很高的挑战性，因为它是从印度公元前 3 世纪左右的非常古老的石刻中采集的。困难的原因在于这些碑文因环境因素和人类长期疏忽而退化。虽然已有许多研究对碑文图像进行了二值化处理，但针对碑文图像的研究却寥寥无几，而且还没有关于对刻写在不规则介质上的碑文进行二值化处理的研究报告。分析结果适用于所有刻在不规则背景上的文字。本文回顾了各种二值化技术在 Tamizhi 碑文图像上的表现。由于之前没有相关工作，我们将现有的二值化算法应用于 Tamizhi 碑文图像，并通过适当的推理分析了这些算法的性能。今后，我们相信这种对结果的推理将有助于新的研究人员调整、组合或设计新的二值化技术。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.