Optimizing Text Recognition in Borehole Log Images Using a Multi-Layout Adjustment Voting Mechanism
Source abstract
Abstract
The borehole log image contains valuable text information, encompassing key geological data such as structural composition, orebody distribution, and lithological characteristics. These data are important for mineral prediction, GeoBigData, and GeoModeling. However, text recognition in borehole log images is challenging due to complex structures, image noise, and diverse fonts, leading to low accuracy with traditional OCR methods. As a result, substantial manual intervention is often required for verification and correction, hindering efficient application. This study proposes an optimization method based on the multi-layout adjustment voting mechanism to improve text recognition accuracy in borehole log images. During the recognition process, multiple OCR results are generated by adjusting text layouts, and a voting mechanism integrates these results to produce the most accurate output. Experimental results on the Dayingezhuang and Dingjiashan datasets demonstrate the effectiveness of the proposed method, achieving F1 scores of 97.96% and 94.36%, respectively. This optimization method improves text recognition accuracy and recall without modifying the OCR algorithm or applying post-processing, providing a new technical approach to enhancing text recognition precision in borehole log images. This improvement in text extraction accuracy from geological borehole data not only facilitates large-scale integration and analysis of subsurface geological information but also provides essential foundational data for GeoBigData and GeoModeling applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Handwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Ministry of Education, Central South University, Changsha 410083, China
Guangzhou Urban Planning & Surveying & Designing Research Institute Co., Ltd., Guangzhou 510060, China
Guangzhou Collaborative Innovation Center of Natural Resources Planning and Marine Technology, Guangzhou 510060, China
Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510030, China
Funding
National Natural Science Foundation of China
References (34)
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