Journal article Open Access

Automated hierarchical classification of scanned documents using convolutional neural network and regular expression

Rifiana Arief; Achmad Benny Mutiara; Tubagus Maulana Kusuma; Hustinawaty

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  <identifier identifierType="URL"></identifier>
      <creatorName>Rifiana Arief</creatorName>
      <affiliation>Gunadarma University</affiliation>
      <creatorName>Achmad Benny Mutiara</creatorName>
      <affiliation>Gunadarma University</affiliation>
      <creatorName>Tubagus Maulana Kusuma</creatorName>
      <affiliation>Gunadarma University</affiliation>
      <affiliation>Gunadarma University</affiliation>
    <title>Automated hierarchical classification of scanned documents using convolutional neural network and regular expression</title>
    <subject>Convolutional neural network hierarchical</subject>
    <subject>Regular expression</subject>
    <subject>Scanned documents</subject>
    <date dateType="Issued">2022-02-01</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.11591/ijece.v12i1.pp1018-1029</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from Pusat Data Teknologi dan Informasi (Technology and Information Data Center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.&lt;/p&gt;</description>
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