色彩模态会影响手写识别吗？一项基于卷积神经网络的波斯语手写体实证研究
Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks
AI总结：
该研究利用含三种色彩模态的波斯语手写数据集，通过CNN实验探究色彩模态对手写识别的影响，发现黑白图像识别表现更优、效率更高，但不同模态准确率无显著差异。
摘要：
现有文献中的大多数手写识别方法都聚焦于黑白（BW）图像数据库并在其上开展评估。本文尝试解答文档识别领域的一个基础问题：我们将卷积神经网络（CNNs）作为视觉模拟工具，探究手写数字与单词的色彩模态是否会影响其识别准确率或速度。据我们所知，由于缺乏同时包含手写体三种色彩模态的手写数据库，该问题此前尚未得到解答。为回答这一问题，我们从一个全新的波斯语手写数据库中选取了13330个孤立数字和62500个单词，该数据库具备三种不同的色彩模态，在规模和多样性上具有独特性。我们将所选数据集划分为训练集、验证集和测试集，随后使用训练样本训练了结构相似的常规CNN模型。测试集的实验结果显示，相较于另外两种色彩模态，CNN在黑白数字与单词图像上的表现更优，但总体而言不同色彩模态下的网络准确率无显著差异。此外，对三种色彩模态下训练时间的对比表明，使用CNN识别黑白图像中的手写数字和单词效率要高得多。
英文摘要：
Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient.
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