Context driven text segmentation and recognition
Pattern Recognition Letters
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Location and interpretation of destination addresses on handwritten Chinese envelopes
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing the K Shortest Paths: A New Algorithm and an Experimental Comparison
WAE '99 Proceedings of the 3rd International Workshop on Algorithm Engineering
An approach for real-time recognition of online Chinese handwritten sentences
Pattern Recognition
Learning-based word spotting system for Arabic handwritten documents
Pattern Recognition
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This paper presents a context driven segmentation and recognition method for handwritten Chinese characters. We follow a split-merge technique in character segmentation. In this process, a Chinese text line is first pre-segmented into a sequence of radicals, which are then merged according to a cost function combining both recognition confidence and contextual cost. Two strategies are also proposed for implementation: bi-gram based merging and lexicon driven merging. In the former one, we generate a set of merging paths which are then evaluated by Viterbi algorithm. The radicals’ best merging method is given by the path with the highest score. In the latter strategy, a lexicon is preset and compared with the radicals to determine both radicals’ merging and candidate character selection. Experiments show that contextual information plays a crucial role in Chinese character segmentation and could obviously improve the segmentation and recognition results.