A Deep-Learning Based Visual Sensing Concept for a Robust Classification of Document Images under Real-World Hard Conditions

Abstract

This paper’s core objective is to develop and validate a new neurocomputing model to classify document images in particularly demanding hard conditions such as image distortions, image size variance and scale, a huge number of classes, etc. Document classification is a special machine vision task in which document images are categorized according to their likelihood. Document classification is by itself an important topic for the digital office and it has several usages. Additionally, different methods for solving this problem have been presented in various studies; their respectively reached performance is however not yet good enough. This task is very tough and challenging. Thus, a novel, more accurate and precise model is needed. Although the related works do reach acceptable accuracy values for less hard conditions, they generally fully fail in the face of those above-mentioned hard, real-world conditions, including, amongst others, distortions such as noise, blur, low contrast, and shadows. In this paper, a novel deep CNN model is developed, validated and benchmarked with a selection of the most relevant recent document classification models. Additionally, the model’s sensitivity was significantly improved by injecting different artifacts during the training process. In the benchmarking, it does clearly outperform all others by at least 4%, thus reaching more than 96% accuracy.

C-RISE: A Post-hoc Interpretation Method of Black-box Models for SAR ATR

“…Additionally, 27 it is challenging to guarantee the effectiveness of recognition results [5]. In recent years, 28 deep learning methods [6], especially Convolutional Neural Networks (CNN), have 29 been extensively used in computer vision [7,8] and demonstrating remarkable achieve-Version April 12, 2023 submitted to Remote Sens.…”

LIME-Based Data Selection Method for SAR Images Generation Using GAN

“…Target recognition is generally considered as the most challenging process in SAR image interpretation because of the complex procedures in the feature extraction and classification [2]. With the development of high-performance computers, deep learning has gradually become popular in computer vision and natural language processing [3]. Deep learning methods autonomously learn the inner relationship between massive labeled data and the corresponding categories.…”