The cultural and regional diversity across the world and specifically in India has given birth to a large number of writing systems and scripts having a variety of character sets. For scripts having a larger character set, just a simple keyboard with limited character set is not the optimal way for providing inputs to the computer. Variations in individual handwriting due to mood swings, changes in medium of writing, changes in writing styles, etc. pose a challenge before the character recognition (CR) research community. Similar kinds of symbols in various scripts and languages act as a big barrier in multilingual CR. Lack of benchmark results and corpora for multilingual CR hinder the research in multilingual CR. There have been only a limited number of articles for optimal combination of features and classifiers to process multilingual data. Multilingual CR has least explored the Indic scripts. This paper presents a detailed review and analysis of the work done in multilingual online as well as offline CR for Indic and non-Indic scripts. The paper mainly contributes in two ways: Firstly, it provides a clear perspective about various phases of monolingual and multilingual CR; and secondly, identifies the major deficiencies in monolingual and multilingual CR for printed and handwritten text. It contributes by giving an in-depth view of work done at each phase including data acquisition, pre-processing, segmentation, feature extraction, recognition and post-processing of CR. Issues to be resolved at each phase have also been elaborated. The recent work done using Deep and Shallow architectures has been analysed. Tools used for these architectures have been compared to highlight their pros and cons. The present work also suggests how further research can be conducted in the field of monolingual and multilingual CR. The problems such as CR in hybrid documents, identifying more reliable features, resolving issues of similar characters, identifying optimal combination strategies for deep and shallow architectures, etc. need to be tackled in future research.

中文翻译：

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使用深度和浅层架构的单语和多语字符识别调查：印度语和非印度语脚本

世界各地，特别是印度的文化和地区多样性，催生了大量具有各种字符集的书写系统和文字。对于具有较大字符集的脚本，仅具有有限字符集的简单键盘并不是向计算机提供输入的最佳方式。由于情绪波动、书写媒介的变化、书写风格的变化等导致的个人笔迹变化对字符识别 (CR) 研究界构成了挑战。各种文字和语言中的类似符号是多语言 CR 的一大障碍。缺乏多语言 CR 的基准结果和语料库阻碍了多语言 CR 的研究。用于特征和分类器的最佳组合来处理多语言数据的文章数量有限。多语言 CR 对印度文字的研究最少。本文详细回顾和分析了在多语言在线和离线 CR 中针对印度语和非印度语脚本所做的工作。该论文主要有两个方面的贡献：第一，它对单语和多语CR的各个阶段提供了清晰的视角；其次，确定印刷和手写文本的单语和多语 CR 的主要缺陷。它通过深入了解在每个阶段所做的工作，包括数据采集、预处理、分割、特征提取、识别和 CR 后处理，做出了贡献。各阶段要解决的问题也作了详细阐述。分析了最近使用 Deep 和 Shallow 架构完成的工作。已对用于这些架构的工具进行了比较，以突出它们的优缺点。目前的工作还建议如何在单语和多语 CR 领域进行进一步的研究。混合文档中的 CR、识别更可靠的特征、解决相似特征的问题、确定深浅层架构的最佳组合策略等问题需要在未来的研究中解决。