公开(公告)号：US06594393B1

公开(公告)日：2003-07-15

申请号：US09569531

申请日：2000-05-12

申请人： Thomas P. Minka ,  Dan S. Bloomberg ,  Ashok C. Popat

发明人： Thomas P. Minka ,  Dan S. Bloomberg ,  Ashok C. Popat

IPC分类号： G06K968

CPC分类号： G06K9/6297 ,  Y10S707/99936

摘要： In a text recognition system, the computational efficiency of a text line image decoding operation is improved by utilizing the characteristic of a graph known as the cut set. The branches of the data structure that represents the image are initially labeled with estimated scores. When estimated scores are used, the decoding operation must perform iteratively on a text line before producing the best path through the data structure. After each iteration, nodes in the best path are re-scored with actual scores. The decoding operation incorporates an operating mode called skip mode. When the number of consecutive image positions for which the change value of cumulative path scores between current and prior iterations is substantially constant and exceeds a threshold, this signals the presence of a cut set, and the score change value is added to a previously computed path score until a re-scored node is encountered, thereby eliminating the expensive computation of new cumulative path scores at those image positions.

摘要翻译： 在文本识别系统中，通过利用称为切割集的图形的特征​​来提高文本行图像解码操作的计算效率。 表示图像的数据结构的分支最初用估计分数标记。 当使用估计分数时，在通过数据结构生成最佳路径之前，解码操作必须在文本行上迭代执行。 每次迭代后，最佳路径中的节点用实际分数重新计分。 解码操作包括称为跳过模式的操作模式。 当当前迭代和以前迭代之间的累积路径得分的变化值基本上恒定并超过阈值的连续图像位置的数量时，这表示切割集合的存在，并将得分改变值添加到先前计算的路径 得分，直到遇到重新计分的节点，从而消除了在这些图像位置处的新累积路径分数的昂贵计算。

公开(公告)号：US06738518B1

公开(公告)日：2004-05-18

申请号：US09570004

申请日：2000-05-12

申请人： Thomas P. Minka ,  Dan S. Bloomberg ,  Ashok C. Popat

发明人： Thomas P. Minka ,  Dan S. Bloomberg ,  Ashok C. Popat

IPC分类号： G06K968

CPC分类号： G06K9/72 ,  G06K2209/01

摘要： In a text recognition system that uses a stochastic finite state network to model a document image layout, the computational efficiency of text line decoding is improved. In a typical implementation, the dynamic programming operation that accomplishes decoding uses actual scores computed between two-dimensional (2D) bitmapped character template images and the (2D) bitmapped observed image. Scoring measures the degree of a match between a character template and the observed image. Computation of these actual scores is replaced with the simpler computation of column-based (i.e., one-dimensional) heuristic scores. Because the column-based heuristic scores can be shown to be a true upper bound on actual template-image scores, the heuristic scores are accurate enough to use in place of actual scoring during text line decoding. The heuristic scores essentially reduce the expensive two-dimensional computation of the actual template-image scores required by prior decoding methods to a simpler but accurate one-dimensional computation.

摘要翻译： 在使用随机有限状态网络对文档图像布局进行建模的文本识别系统中，文本行解码的计算效率得到提高。 在典型的实现中，完成解码的动态编程操作使用在二维（2D）位图匹配的字符模板图像和（2D）位图观察图像之间计算的实际分数。 评分测量字符模板与观察图像之间的匹配程度。 这些实际分数的计算由基于列（即，一维）启发式分数的更简单的计算代替。 因为基于列的启发式分数可以显示为实际模板图像分数的真实上限，所以启发式分数足够准确地用于代替文本行解码期间的实际评分。 启发式分数基本上将先前解码方法所需的实际模板图像分数的昂贵的二维计算减少到更简单而准确的一维计算。