<![CDATA[Handwritten notes remain widely used as transaction proofs, yet extracting information from them automatically is challenging due to variations in handwriting styles, layouts, and image qualities. This research aims to develop a transaction information extraction method for handwritten notes by integrating Optical Character Recognition (OCR) and Named Entity Recognition (NER). The research stages include image preprocessing using grayscale conversion, denoising, and Contrast Limited Adaptive Histogram Equalization (CLAHE), text recognition using EasyOCR across multiple image orientations, and transaction entity identification using IndoBERT with the BIO scheme. OCR performance is evaluated using Character Error Rate (CER) and Word Error Rate (WER), while NER is evaluated using Accuracy, Precision, Recall, and F1-score. Test results show that the NER model achieved an Accuracy of 0.8889, Precision of 0.7857, Recall of 0.8462, and F1-score of 0.8148 on the test data. However, the OCR-NER integration still exhibits character recognition errors and fails to completely extract all transaction attributes, particularly PRICE and AMOUNT. These findings indicate that the OCR-NER integration holds potential for supporting transaction data digitization in handwritten notes, though improving OCR quality and using a larger dataset are necessary to enhance system accuracy and generalizability.]]>
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