Abstract:
This paper proposes a novel approach for scanned
document categorization using a deep neural network
architecture. The proposed approach leverages the strengths of
both convolutional neural networks (CNNs) and recurrent neural
networks (RNNs) to extract features from the scanned documents
and model the dependencies between words in the documents.
The pre-processed documents are first fed into a CNN, which
learns and extracts features from the images. The extracted
features are then passed through an RNN, which models the
sequential nature of the text. The RNN produces a probability
distribution over the predefined categories, and the document is
classified into the category with the highest probability. The
proposed approach is evaluated on a dataset of scanned
documents, where each document is categorized into one of four
predefined categories. The experimental results demonstrate that
the proposed approach achieves high accuracy and outperforms
existing methods. The proposed approach achieves an overall
accuracy of 97.3%, which is significantly higher than the existing
methods' accuracy. Additionally, the proposed approach's
performance was robust to variations in the quality of the
scanned documents and the OCR accuracy. The contributions of
this paper are twofold. Firstly, it proposes a novel approach for
scanned document categorization using deep neural networks
that leverages the strengths of CNNs and RNNs. Secondly; it
demonstrates the effectiveness of the proposed approach on a
dataset of scanned documents, highlighting its potential
applications in various domains, such as information retrieval,
data mining, and document management. The proposed
approach can help organizations manage and analyze large
volumes of data efficiently
