van der Zant Tijn, Schomaker Lambert, Haak Koen
AI Department, University of Groningen, Postbus 407, 9700 AK Groningen, The Netherlands.
IEEE Trans Pattern Anal Mach Intell. 2008 Nov;30(11):1945-57. doi: 10.1109/TPAMI.2008.144.
For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.
对于快速访问新的手写文集而言，当前的手写识别方法过于繁琐。它们无法处理标记数据不足的问题，并且针对每种手写体、风格、语言和文集都需要进行大量的实验室训练。我们提出了一种受生物学启发的全词识别方法，该方法用于在一个名为Monk的基于网络的实时注释系统中逐步引出单词标签。鉴于海量的图像数据，应尽量减少人工操作，因此依靠机器中强大的感知机制变得很重要。最近的视觉神经生理学计算模型被应用于孤立单词分类。一种类似灵长类动物皮层的机制能够对出现频率较低的文本图像进行分类。通常，这些图像最难检索，并且常常包含命名实体，对人们来说被视为最重要的。如果没有足够的标记实例，标准的模式识别技术通常无法处理这些文本图像。该检索系统的结果与归一化单词图像匹配进行了比较，结果看起来很有前景。
