An Approach to Extract Information from Academic Transcripts of HUST

EasyChair Preprint 10034

Abstract

In many Vietnamese schools, grades are still being inputted into the database manually, which is not only inefficient but also prone to human error. Thus, the automation of this process is highly necessary, which can only be achieved if we can extract information from academic transcripts. In this paper, we test our improved CRNN model in extracting information from 126 transcripts, with 1008 vertical lines, 3859 horizontal lines, and 2139 handwritten test scores. Then, this model is compared to the Baseline model. The results show that our model significantly outperforms the Baseline model with an accuracy of 99.6% in recognizing vertical lines, 100% in recognizing horizontal lines, and 96.11% in recognizing handwritten test scores.

Keyphrases: Academic transcript, CRNN, CTC, Digit string recognition, image processing

@booklet{EasyChair:10034,
  author    = {Nguyen Quang Hieu and Nguyen Le Quy Duong and Le Quang Hoa and Nguyen Quang Dat},
  title     = {An Approach to Extract Information from Academic Transcripts of HUST},
  howpublished = {EasyChair Preprint 10034},
  year      = {EasyChair, 2023}}