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.

通过使用卷积神经网络（CNN）作为模拟器，我们研究了手写数字和单词的彩色模态是否会影响它们的识别准确性或速度。实验结果表明，相对于其他两种彩色模态，黑白图像上的CNN在数字和单词的识别上具有更好的性能，而不同彩色模态下的网络精度没有显著差异。此外，通过比较三种彩色模态的训练时间，我们发现使用CNN在黑白图像上识别手写数字和单词更加高效。

颜色模态对手写识别的影响：基于卷积神经网络的波斯语手写实证研究