Study on the Digitization Support System for Historical Documents

古文常因年久失修而出现虫洞和变色现象，有时还会出现印章和批注重叠的现象。这使得字符提取和识别变得困难。此外，古典文本中的字符通常是草书。因此，将草书字符串分割成字符非常重要。针对历史文献预处理中存在的上述问题，研究了新的字符分割方法。该方法首先进行一些滤波，即使用颜色滤波器根据字符的颜色提取候选像素，使用降噪滤波器，使用转换器创建灰度图像，然后对图像进行二值化。通过对周边投影直方图的分析，可以获得文本是垂直还是水平书写以及页面中平均字符大小的布局信息。字符是由像素逐渐构成的。最后，草书字符串的分割基本上是沿着连接同一轮廓上最近凹点的…More线进行的。新方法的优点避免了对特定语言的字符样式知识和布局信息的需要。上述方法的缺陷是结果受局部轮廓形状的强烈影响。为了弥补这一问题，提出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 （G^*I）对原始图像I进行模糊处理，然后应用△^2(G^*I) =（△^2G）^*I=O等拉普拉斯算子△^2得到边缘。高斯函数表现为一个带通滤波器，在小于参数σ（标准差）的尺度上消除小结构。当σ变大时，图像变得粗糙。如果一个凹度在一个粗糙的图像中守恒意味着在那里形状发生了很大的变化。重要的问题是，粗糙图中较大的形状变化在细节图中也是守恒的，即粗糙图中的分离线一定存在于细节图中。实验表明，该方法在选择合适的线段分割草书字符串方面具有较强的鲁棒性。为了简化这一过程，引入了小波多分辨率分析。利用页面布局信息提取标题，利用n-gram识别手写字符，作为全字符识别的初步研究。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 Due to the aforementioned problems in preprocessing historical documents, new means of character segmentation are examined.The proposed method begins with some filtering, i.e., a color filter to extract candidate pixels of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