Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solv...Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.展开更多
要:提出一种基于掩码区域卷积神经网络的文本检测模型。首先从扩大模型感受野并尽可能保持模型效率的角度出发,针对残差神经网络中的瓶颈结构进行优化,构建基于结构优化的残差神经网络(residual network based on structural optimizati...要:提出一种基于掩码区域卷积神经网络的文本检测模型。首先从扩大模型感受野并尽可能保持模型效率的角度出发,针对残差神经网络中的瓶颈结构进行优化,构建基于结构优化的残差神经网络(residual network based on structural optimization,ResNetSO);然后去除冗余特征以提高融合后特征质量,并将空间注意力机制应用于特征金字塔网络,构建了基于下层特征指导的特征金字塔网络(feature pyramid network based on lower feature guidance,FPNetLFG)。在两个公开数据集上的实验结果表明:包含ResNetSO和FPNetLFG两个模块的模型应用在级联区域卷积神经网络、递归特征金字塔和可切换空洞卷积的目标检测模型中,分别可以带来0.8%和0.3%左右的F1值提升,从而说明了该方法的有效性和普遍适用性。展开更多
文摘Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.
文摘要:提出一种基于掩码区域卷积神经网络的文本检测模型。首先从扩大模型感受野并尽可能保持模型效率的角度出发,针对残差神经网络中的瓶颈结构进行优化,构建基于结构优化的残差神经网络(residual network based on structural optimization,ResNetSO);然后去除冗余特征以提高融合后特征质量,并将空间注意力机制应用于特征金字塔网络,构建了基于下层特征指导的特征金字塔网络(feature pyramid network based on lower feature guidance,FPNetLFG)。在两个公开数据集上的实验结果表明:包含ResNetSO和FPNetLFG两个模块的模型应用在级联区域卷积神经网络、递归特征金字塔和可切换空洞卷积的目标检测模型中,分别可以带来0.8%和0.3%左右的F1值提升,从而说明了该方法的有效性和普遍适用性。
