Subjective question intelligent marking method and system based on depth learning, and storage medium

Abstract

The invention discloses a subjective question intelligent marking method and system based on depth learning, and a storage medium. The method includes the steps of obtaining the image of an answer sheet, preprocessing the acquired image; using OpenCV image segmentation processing to segment the answer sheet image, wherein the image is divided into an objective question answer area and a subjectivequestion answer area; then, using an OMR mode to identify the answer area of objective questions, and using an OCR mode to identify the answer area of subjective questions; inputting the standard answers of objective and subjective questions into a database, wherein the subjective questions comprise subjective questions with standard answers and subjective questions without standard answers; andcounting the scores of the objective and subjective questions in turn. If test paper with an abnormal score is found in the marking process, manual marking intervention is needed to achieve the correction of the abnormal paper.

Description

technical field [0001] The present invention relates to the field of computer-aided examination papers, in particular to a method, system and storage medium for intelligent examination papers of subjective questions based on deep learning. Background technique [0002] In recent years, the answer sheet recognition system has been well-known by the society for many years and has been developing and progressing. With the advent of the big data era and cloud computing, online marking is also gradually improved and perfected according to needs. At present, it mainly includes traditional optical character recognition OCR (Optical Character Recognition) and optical mark recognition OMR (Optical Mark Recognition). Input it into the computer, and carry out an effective recognition process on the information in the image. [0003] OCR (Optical Character Recognition) method: first, scan and record the information to be processed or other documents through the acquisition tool; then p...

AI Technical Summary

Problems solved by technology

At the same time, OMR also has some limitations in practical applications. If the "painted spots" on the information card are not completely aligned with the electric eye, it cannot be accurately identified, that is, it cannot be accurately identified when the information card is tilted; the wrinkled information card cannot be identified; Paper with low printing quality and information cards with low quality paper itself cannot be recognized; the marking must be filled in according to the specifications, otherwise the recognition effect will be greatly affected

Therefore, in the actual application environment, the information card is scanned and imaged by the scanner, and there will be recognition errors when it is tilted

Images