Chart Visualizations (CharVis) such as charts/plots and diagrams are commonly used in documents for representing the underlying quantitative information. However, the inaccessibility of such visualizations exemplify one of the rife challenges of information access for Blind and Visually Impaired People (BVIP). The existing BVIP-related assistive technologies (ATs) are capable enough to provide the accessibility of textual components; however, for CharVis, it is of concern. Unlike textual components, CharVis comprise critical compressed data and requires perspicacious reverse-engineering schemes to output the raw data table used initially for creation. An intelligent and automated BVIP-compatible CharVis understanding scheme requires extraction of raw underlying data and presenting it into BVIP-compatible representation, i.e., summarized audio form. With the recent advancements in rapidly-growing AI-domain, several frameworks have been proposed in the literature for accurate extraction of the raw content from CharVis. Most of the existing related work and surveys emphasize only the visualization-related aspects without considering the apprehensions regarding inclusivity of these visualization schemes for BVIP. This survey aims to analyze various research methodologies on the CharVis understanding process, and related existing and potential assistive applications in a threefold outcome manner - (1) Research: We provide a perspicuous investigation of state-of-the-art research methodologies for CharVis understanding. (2) Applications: We provide a detailed rubric analysis of various applications and compatible assistive solutions (3) Gap: We summarize the challenges and the gaps between the research and application domain and provides new insights/pointers for future research. Additionally, the survey presents a consolidated list of datasets, software/apps, and hardware sources for supporting future research.

图表可视化 ( CharVis )（例如图表/绘图和图表）通常在文档中用于表示基础定量信息。然而，此类可视化的不可访问性体现了盲人和视障人士 (BVIP) 信息访问的普遍挑战之一。现有的 BVIP 相关辅助技术 (AT) 足以提供文本组件的可访问性；然而，对于CharVis来说，这是令人担忧的。与文本组件不同，CharVis包含关键的压缩数据，并且需要有洞察力的逆向工程方案来输出最初用于创建的原始数据表。智能且自动化的 BVIP 兼容CharVis理解方案需要提取原始底层数据并将其呈现为 BVIP 兼容的表示形式，即摘要音频形式。随着人工智能领域快速发展的最新进展，文献中提出了几种框架，用于从CharVis中准确提取原始内容。大多数现有的相关工作和调查只强调与可视化相关的方面，而没有考虑对 BVIP 的这些可视化方案的包容性的担忧。本调查旨在分析关于CharVis的各种研究方法理解过程，以及以三重结果方式相关的现有和潜在辅助应用 - (1) 研究：我们对CharVis理解的最先进研究方法进行了明确的调查。(2) Applications：我们提供了各种应用程序和兼容辅助解决方案的详细规则分析 (3) Gap：我们总结了研究和应用领域之间的挑战和差距，并为未来的研究提供了新的见解/指针。此外，该调查还提供了数据集、软件/应用程序和硬件来源的综合列表，以支持未来的研究。