LSTM Network and OCR Performance for Classification of Decimal Dewey Classification Code

Abstract

Background of the study: Giving book code by a librarian in accordance with the Decimal Dewey Classification system aims to facilitate the search for books on the shelf precisely and quickly. Purpose: The first step in giving code to determine the class of books is the principal division which has 10 classes. Method: This study proposed Optical Character Recognition to read the title text on the book cover, preprocessing the text, and classifying it by Long Short-Term Memory Neural Network. Findings: In general, a librarian labeled a book by reading the book title on the book cover and doing book class matching with the book guide of DDC. Automatically, the task requires time increasingly. We tried to classify the text without OCR and utilize OCR which functions to convert the text in images into text that is editable. BY the experimental result, the level of classification accuracy without utilizing OCR is higher than using OCR. Conclusion: The magnitude of the accuracy is 88.57% and 74.28% respectively. However, the participation of OCR in this classification is quite efficient enough to assist a beginner librarian to overcome this problem because the accuracy difference is less than 15%.