Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JAIS (Journal of Applied Intelligent System)
Susanto, Al Birr Karim
Unknown Affiliation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparison of String Similarity Algorithm in post-processing OCR Susanto, Al Birr Karim; Muliadi, Nuraziz; Nugroho, Bagus; Muljono, Muljono
Journal of Applied Intelligent System Vol. 8 No. 1 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i1.7079

Abstract

The Optical Character Recognition (OCR) problem that often occurs is that the image used, has a lot of noise covering letters in a word partially. This can cause misspellings in the process of word recognition or detection in the image. After the OCR process, we must do some post-processing for correcting the word. The words will be corrected using a string similarity algorithm. So what is the best algorithm? We conducted a comparison algorithm including the Levenshtein distance, Hamming distance, Jaro-Winkler, and Sørensen – Dice coefficient. After testing, the most effective algorithm is the Sørensen-Dice coefficient with a value of 0.88 for the value of precision, recall, and F1 score
Conditional Matting For Post-Segmentation Refinement Segment Anything Model Susanto, Al Birr Karim; Soeleman, Moch Arief; Budiman, Fikri
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9024

Abstract

Segment Anything Model (SAM) is a model capable of performing object segmentation in images without requiring any additional training. Although the segmentation produced by SAM lacks high precision, this model holds interesting potential for more accurate segmentation tasks. In this study, we propose a Post-Processing method called Conditional Matting 4 (CM4) to enhance high-precision object segmentation, including prominent, occluded, and complex boundary objects in the segmentation results from SAM. The proposed CM4 Post-Processing method incorporates the use of morphological operations, DistilBERT, InSPyReNet, Grounding DINO, and ViTMatte. We combine these methods to improve the object segmentation produced by SAM. Evaluation is conducted using metrics such as IoU, SAD, MAD, Grad, and Conn. The results of this study show that the proposed CM4 Post-Processing method successfully improves object segmentation with a SAD evaluation score of 20.42 (a 27% improvement from the previous study) and an MSE evaluation score of 21.64 (a 45% improvement from the previous study) compared to the previous research on the AIM-500 dataset. The significant improvement in evaluation scores demonstrates the enhanced capability of CM4 in achieving high precision and overcoming the limitations of the initial segmentation produced by SAM. The contribution of this research lies in the development of an effective CM4 Post-Processing method for enhancing object segmentation in images with high precision. This method holds potential for various computer vision applications that require accurate and detailed object segmentation.
Co-Authors Fikri Budiman Moch Arief Soeleman, Moch Arief Muliadi, Nuraziz Muljono Muljono Nugroho, Bagus