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Information Extraction with the Optimized Selective Kernel-Based Deep Learning from the Unstructured Invoice
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AI
DOI：10.1007/s00354-025-00311-7.png)
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Abstract
En 中文
The timely processing and handling of invoicing papers linked to COVID-19 has become essential in light of the epidemic. The differences between the text and their complex meanings remain the major issue of the conventional approaches to managing the documents. To overcome these issues, this research proposed an Attacker Grouping Optimized Selective Kernel-based Deep Convolutional Neural Network (AG optimized SK-DCNN) model and optimize the invoice processing workflow. The selective kernel network is utilized to obtain accurate data from invoice documents that are unstructured, whereas the Google Optical Character Recognition (Google-OCR) makes it possible to extract text from images that may be the photographs or the scanned images, with advanced technology created by Google. Text annotation with Named Entity Recognition (NER) necessitates detection along with categorizing structures of the available text that aids in achieving the efficient extraction of the data. The feature extraction and classification duties are ensured by the AG-optimized SK-DCNN, where the AGO utilizes the aggregating and foraging characteristics to enhance the system performance and reduce the error rate occurrence. The AG-optimized SK-DCNN approach achieves 94.7%, 95.92%, and 95.02% for accuracy, sensitivity, and specificity, respectively.
Keywords:
Invoice documents
Selective kernel-based DCNN
Google OCR
Named entity recognition
Attacker grouping optimization
Journal
N
IF:
2.8
Papers:
24
Citations:
652