When IC meets text: Towards a rich annotated integrated circuit text dataset
Document Type
Article
Publication Date
3-1-2024
Abstract
Automated Optical Inspection (AOI) is a process that uses cameras to autonomously scan printed circuit boards for quality control. Text is often printed on chip components, and it is crucial that this text is correctly recognized during AOI, as it contains valuable information. In this paper, we introduce ICText, the largest dataset for text detection and recognition on integrated circuits. Uniquely, it includes labels for character quality attributes such as low contrast, blurry, and broken. While loss-reweighting and Curriculum Learning (CL) have been proposed to improve object detector performance by balancing positive and negative samples and gradually training the model from easy to hard samples, these methods have had limited success with one-stage object detectors commonly used in industry. To address this, we propose Attribute-Guided Curriculum Learning (AGCL), which leverages the labeled character quality attributes in ICText. Our extensive experiments demonstrate that AGCL can be applied to different detectors in a plug-and-play fashion to achieve higher Average Precision (AP), significantly outperforming existing methods on ICText without any additional computational overhead during inference. Furthermore, we show that AGCL is also effective on the generic object detection dataset Pascal VOC. Our code and dataset will be publicly available at https://github.com/chunchet-ng/ICText-AGCL.
Keywords
Attribute-guided curriculum learning, Optical character recognition, Integrated circuit text dataset
Divisions
universiti
Funders
Universiti Malaya International Collaboration Grant [ST099-2022]
Publication Title
Pattern Recognition Letters
Volume
147
Publisher
Elsevier
Publisher Location
125 London Wall, London, ENGLAND