
Handwritten Documents Validation using Pattern Recognition and Transfer Learning
Publication year - 2022
Publication title -
international journal of web-based learning and teaching technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 12
eISSN - 1548-1107
pISSN - 1548-1093
DOI - 10.4018/ijwltt.20220901oa01
Subject(s) - computer science , naive bayes classifier , artificial intelligence , transfer of learning , convolutional neural network , support vector machine , artificial neural network , pattern recognition (psychology) , signature (topology) , machine learning , data mining , geometry , mathematics
Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.