Study on the Digitization Support System for Historical Documents

古典文献因年代久远，常出现虫蛀、变色等现象，文字上有时会出现印章、批注重叠的现象。这使得字符提取和识别变得困难。此外，古典文献中的字符通常是草书，因此，将草书字符串分割成字符是很重要的。针对历史文档预处理中存在的上述问题，本文提出了一种新的字符分割方法，该方法首先进行滤波，一彩色滤波器，用以根据字符的颜色来抽取字符的候选像素;一些噪声降低滤波器;一转换器，用以产生灰度图像，然后二值化图像。从外围投影直方图的分析中获得关于文本是垂直还是水平书写的布局信息以及页面中的平均字符大小。一个字符是由像素逐渐构成的。最后，基本上沿着沿着 ...更多信息 连接同一轮廓上最近的凹部的线。新方法的优点是避免了对特定语言的字符风格知识和布局信息的需要，而上述方法的缺点是结果受轮廓局部形状的强烈影响。为了弥补这一问题，引入了一种多分辨率分析方法。其基本思想是，通过卷积高斯函数G（例如G^*I）来模糊原始图像I，然后应用拉普拉斯算子（例如）来获得边缘。高斯函数表现为带通滤波器，其消除尺度小于参数σ（标准偏差）的小结构。当σ变大时，图像变得粗糙。如果在粗糙的图片中保留一个常数意味着形状在那里发生了很大的变化。重要的问题是，在粗糙图像中的大的形状变化在细节图像中也是保守的，即，在粗糙图像中的分离线必须存在于细节图像中。实验结果表明，该方法在选取合适的线段对草书字符串进行分割方面比上述方法具有更好的鲁棒性。本文引入了小波多分辨率分析方法，并利用版面信息提取标题，利用n-gram识别手写体字符，作为全字符识别的初步试验。少

Classical papers often suffer from wormholes and discoloration due to aging and there are sometimes seals and annotations overlapped on characters. These make character extraction and recognition difficult. Moreover, characters in classical texts are often cursive.Thus, the segmentation of a cursive string into characters is important. Due to the aforementioned problems in preprocessing historical documents, new means of character segmentation are examined.The proposed method begins with some filtering, i.e., a color filter to extract candidate pixels of characters according to their color, some noise reduction filters, a converters to create gray images then binarization images. Layout information as to whether a text is written vertically or horizontally as well as average character size in a page are obtained from the analysis of a peripherally projected histogram. A character is constructed gradually from pixels. At last, segmentation of a cursive string is done basically along the … More line connecting the nearest concavities on the same contour. The strength of the new methods avoids the need for language specific character style knowledge and layout information.The defect of aforementioned procedure is that results are strongly affected by local shapes of contours. To compensate for this problem, a kind of multi-resolution analysis method is introduced. The basic idea is that an original image I is blurred by convoluting a Gaussian function G such as G^*I, then Laplacian operator ▽^2 is applied such as ▽^2(G^*I) = (▽^2G)^*I=O to get edges. The Gaussian function behaves as a band pass filter that wipes out small structures at scale less than the parameter σ (standard deviation). When σ becomes larger, a picture becomes rougher. If a concavity conserved in a rougher picture means that shape changed largely around there. The important issue is that the large change of shape in a rougher picture is also conserved in the detail picture, that is, separation lines in a rougher picture must exist in the detail picture. Experiments showed that this method is robuster than aforementioned to choose appropriate lines to segment cursive string. Multi-resolution analysis by wavelet is introduced to facilitate this procedure.Also, extracting titles using page layout information and recognizing hand-written characters using n-gram were done as the preliminary examinations for full character recognition. Less