System and Method for Automated Index Generation for Answer Booklets Using Deep Learning

Abstract

The invention is an automated system for document layout analysis and indexing educational answer booklets using a deep learning approach. First, we collect JPEG images of answer booklet pages for training, labeling them with region boundaries in MS COCO format. We train a YOLOv3-DLA (Deep Learning-based Object Detection Model) to identify specific regions like question numbers and text areas. During inference, the trained model analyzes new answer booklets to identify these regions, refining any overlapping areas. Tesseract OCR processes the detected regions to extract text information, converting page and question numbers into text. The system then identifies the start and end pages of each answer, generating a structured index in JSON format. This indexed output streamlines the evaluation process by providing a clear reference to specific questions and answers in the booklet, which enhances efficiency in online paper grading systems.

Specification

Description:The automated document layout analysis and indexing system leverages deep learning and OCR technologies to streamline the processing of handwritten documents, specifically focusing on educational answer booklets. Using a combination of object detection models (like YOLOv3) and OCR tools (such as Tesseract), the system identifies key elements such as question numbers, answer sections, and page identifiers in scanned answer sheets. It then generates a structured index of the content, capturing the start and end pages for each answered question, ultimately creating a JSON-based output for easy retrieval. This approach addresses common challenges in manual document processing, including the time-consuming nature of indexing and the risk of human errors. It enables educational institutions to automate the grading process, allowing for faster evaluation and better organization of student responses. Beyond education, this system has potential applications in various industries, such as legal document management, healthcare records indexing, and financial data processing. Despite its advantages, the model faces limitations with handwriting variability, document quality, and generalization to diverse document types, highlighting areas for future enhancements. Overall, this technology represents a significant step toward digitizing and automating the analysis of complex, handwritten documents.
The automated document layout analysis and indexing model significantly enhances online paper valuation systems by streamlining the evaluation of scanned handwritten answer booklets. Traditional manual review of answer sheets involves flipping through pages to locate specific responses, which is time-consuming and prone to errors. This model uses deep learning techniques and OCR to automatically detect question numbers, identify where answers begin and end, and generate a structured index of the entire booklet. Figure 1 describes the proposed work phases. By providing this organized view, it allows examiners to quickly access specific answers, eliminating the need to manually search through multiple pages. This speeds up the grading process, improves consistency, and reduces human errors, ensuring a fairer evaluation. Additionally, the output can be easily integrated into online grading platforms, enabling smooth navigation between questions and responses. For large-scale examinations, this approach scales effectively, handling thousands of answer sheets with ease, significantly reducing the time required for evaluation. Furthermore, it supports better record-keeping by digitizing and indexing student responses, making it easier to retrieve and review answers for audits or performance analysis. Overall, the model enhances efficiency, accuracy, and scalability in online paper valuation systems, supporting digital transformation and improving the grading experience for educators. , Claims:Claims:
1. Document Layout Analysis on Answer Booklets
Document Layout Analysis on answer booklets involves using deep learning models to automatically detect and segment key elements like question numbers, answer regions, and page numbers. This process helps in structuring and indexing handwritten answer sheets, enabling efficient navigation and faster evaluation during grading, especially in online paper valuation systems.
2. Index generation of a Answer Booklet
Index generation of an answer booklet involves automatically identifying the start and end pages of each answer using detected question numbers and regions. This structured indexing creates a navigable reference for each question, allowing examiners to quickly access specific responses, thus streamlining the evaluation and grading process in digital systems.
3. Detection of starting and ending of answer for a question on booklet.
Detection of the starting and ending of an answer in a booklet involves identifying the page where a response begins and the page where it concludes. Using object detection and OCR, the system locates question numbers and analyzes content flow, enabling accurate segmentation of each answer for streamlined indexing and evaluation.

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