资源列表
text_line_extraction_code
- 一个国际会议的历史文档文本行提取的程序，对研究少数民族文档的识别有帮助，可以学习，可以切分倾斜 手写的文字-An international conference of the historical document line of the line of the extraction process, the study of the identification of minority documents helpful, you can learn, you can cut the han
page_segmentation-master
- 它的目的是提取从文本区域的单个文本行手稿页实现文本行分割-Its purpose is to extract a single line of text the text area of the manuscr ipt page to achieve text segmentation
matlab-code
- 主要通过二维码识别相关信息，通过多测试,能够在matlab环境下运行，推荐2010a版本-matlab code use to recognise
license-plate-recognition
- 车牌识别系统设计 包含文档 程序 和测试图像-License plate recognition system design includes documentation procedures and test images
EFG_1D
- 一维无网格法求解悬臂梁的应力、应变和位移，对初学者有帮助-Solving one-dimensional grid cantilever stress, strain and displacement, be helpful for beginners
PCA
- 基于PCA进行人脸识别的Matlab代码，里面包含注释-Face recognition based on PCA Matlab code, which contains notes
leaves-classfy-
- matlab的树叶识别的程序，里面包含注释，是英文的-Matlab leaf recognition procedures, which contains notes, is English
gesture-recognition
- matlab编的手势识别的小程序，简单肤色阈值算法，提取轮廓-Matlab series of gesture recognition of small procedures, a simple skin color threshold algorithm to extract contours
SPM_SC
- This package contains the Matlab codes implementing the ScSPM algorithm described in CVPR 09 paper Linear Spatial Pyramid Matching using Sparse Coding for Image Classification .基于空间金字塔匹配的稀疏编码，用于图像检索，识别与分类-This package contains the Matlab codes im
SAE_DBN_CNNToolbox
- 多种深度学习框架，主要包括堆栈稀疏自动编码器，深信度网络，卷积神经网络等。对于灰度图像和高维图像，展现非常强大的学习性能。-A variety of deep learning framework, including automatic stack sparse encoder, is convinced of the network, convolution neural networks. For grayscale images and high-dimensional image, s
CNN-pooling-strategy
- 基于卷积层和池化层的卷积深度网络被执行，该框架可以有效地识别灰度图像，彩色图像和高光谱图像。- Convolution deep network based on convolution layer and pooling layer is performed, the framework can effectively identify grayscale images, color images and hyperspectral images.
53607888mnistclassify_DBN
- 目前深度学习已经得到研究者的广泛关注，在这里深信度网络被用于手写字符的识别，得到非常好的分类精度。-Currently deep learning has been much attention, here we are convinced of the network is used to identify handwritten characters, get very good classification accuracy.