How to automatically identify and extract table and chart data from files?

To automatically identify and extract table and chart data from files, you can use Optical Character Recognition (OCR) and document parsing technologies. These tools can recognize structured data in formats like PDFs, images, or scanned documents. For tables, algorithms can detect grid lines, headers, and cell contents. For charts, image recognition can identify visual elements like axes, labels, and data points, while OCR can extract associated text.

Steps to achieve this:

1. Preprocessing: Convert files into a readable format (e.g., rasterize images or extract text layers from PDFs).
2. Table Detection: Use algorithms to locate tables by identifying grid structures or semantic patterns.
3. Data Extraction: Extract cell contents using OCR or direct parsing for digital files.
4. Chart Analysis: Use computer vision to detect chart types (e.g., bar, line, pie) and extract labels, values, and titles.
5. Post-processing: Clean and structure the extracted data for further use.

Example:
A financial report PDF contains tables of quarterly revenue and a bar chart showing sales trends. An automated system would:

• Detect the table region, extract rows/columns, and convert it into a structured CSV.
• Recognize the chart’s axes, bars, and legend, then extract numerical values and labels.

For scalable solutions, Tencent Cloud's Document Understanding Service (DUS) can automate this process. It supports table extraction, chart analysis, and multi-format document parsing, ideal for businesses handling large volumes of unstructured data.