DocVQA

DocVQA, short for Document Visual Question Answering, is a field within multimodal artificial intelligence focused on answering questions about the content of document images. It combines optical character recognition (OCR) to extract text with reasoning over the extracted text and the document layout to produce an answer. The problems typically involve documents such as forms, receipts, invoices, reports, manuals, or scanned pages, where the answer may be a word, a number, or a short phrase.

Tasks in DocVQA usually present an image of a document and a natural language question. The system must locate and read relevant text regions, understand the document structure (e.g., tables, headers, columns), and generate a correct answer. Datasets accompanying DocVQA provide pairs of document images and questions with corresponding answers, and evaluation uses metrics such as exact-match accuracy or F1 score depending on the task.

Approaches often integrate OCR outputs with multimodal reasoning modules. Methods include text-aware transformers, graph-based models that reason over text and layout, and end-to-end architectures that learn to read and comprehend documents. Challenges include noisy OCR, diverse layouts, non-standard fonts, handwriting, tabular data, and multi-page documents. DocVQA aims to automate information extraction and document processing across domains such as finance, administration, and research.