When IC meets text: Towards a rich annotated integrated circuit text dataset

• Published in: Pattern recognition Vol. 147; p. 110124
• Main Authors: Ng, Chun Chet, Lin, Che-Tsung, Tan, Zhi Qin, Wang, Xinyu, Kew, Jie Long, Chan, Chee Seng, Zach, Christopher
• Format: Journal Article
• Language: English
• Published: 01.03.2024
• Subjects: Attribute-guided curriculum learnin; Integrated circuit text dataset; Optical character recognition; Text detection; Text Recognition
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2023.110124

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 \textit{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 \textit{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 \textit{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 \href{https://github.com/chunchet-ng/ICText-AGCL}{https://github.com/chunchet-ng/ICText-AGCL}.