Checkbox Detection and Checked State Extraction from Form Documents

Abstract

This paper explains how to detect if a checkbox on a document or a given form and how to grab the checked state of a checkbox if it is present on a given form. Processes are critical to businesses in the finance, healthcare, legal, and e-commerce industries, and many forms are processed daily. It also tackles form designs that differ, standard result checkboxes, and poor-quality pictures that lower the degree to which it is possible. The paper discusses further techniques, such as Optical Character Recognition (OCR) and deep learning models, such as Convolutional Neural Networks (CNNs), which allow for improved accuracy when dealing with complex form layouts for checkbox detection, extraction of checked state, ensuring integrity, and minimizing errors, especially in healthcare and finances. The speed increase and elimination of errors are also demonstrated in specific applications in which automation is applied. Case studies show the practical benefits of automated checkbox detection systems in Canada's healthcare, e-government, and financial sectors. The paper discusses ethical and legal considerations of these items, primarily data privacy, security, and adherence to GDPR and HIPAA regulations. The paper finally outlines the future of detecting checkbox technology to enhance the scalability and speed of form processing systems through AI, deep learning, and cloud computing.