ChartParser: Automatic Chart Parsing for Print-Impaired
Infographics are often an integral component of scientific documents for reporting qualitative or quantitative findings as they make it much simpler to comprehend the underlying complex information. However, their interpretation continues to be a challenge for the blind, low-vision, and other print-impaired (BLV) individuals. In this paper, we propose ChartParser, a fully automated pipeline that leverages deep learning, OCR, and image processing techniques to extract all figures from a research paper, classify them into various chart categories (bar chart, line chart, etc.) and obtain relevant information from them, specifically bar charts (including horizontal, vertical, stacked horizontal and stacked vertical charts) which already have several exciting challenges. Finally, we present the retrieved content in a tabular format that is screen-reader friendly and accessible to the BLV users. We present a thorough evaluation of our approach by applying our pipeline to sample real-world annotated bar charts from research papers.
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Character Recognition (OCR)Similar Papers 제목 키워드 기반
ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats
Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus on narrow chart types and leave diagramma…
ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries
Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user. A large and well-structured dataset is al…
Data SummarizationHallucinationChart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights. Interpreting and making sense of such visualizations can be challenging for some people, such …
Data-to-Text GenerationDecoderText GenerationCycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
Current chart-related tasks, such as chart generation (NL2Chart), chart schema parsing, chart data parsing, and chart question answering (ChartQA), are typically studied in isolation, preventing models from learning the …
Chart Question AnsweringOneChart: Purify the Chart Structural Extraction via One Auxiliary Token
Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and so forth. Even advanced large vision-language models (LVLMs) with billions of parameters struggle to handle such tasks satisf…
Decoder