A Novel Word-Spotting Method for Handwritten Documents Using an Optimization-Based Classifier

Word spotting is the answer to the question whether the document contains the user’s query word. One of the main challenges of keyword spotting at the testing stage is that some testing non-classes are not included in training classes. Hence, this paper presents a robust handwritten word-spotting method for handwritten documents using genetic programming (GP). Using this technique, a tree is created as a classifier which separates the target class (keyword) from the other classes (non-keyword). The new components of the proposed classifier include proper chromosome and new classification fitness function. The proposed chromosome was based on the relationship between features and each chromosome (tree) mapped the features to a real number. Then, a margin was obtained from the real number. To evaluate the generality of the proposed method, several experiments have been designed and implemented on three standard datasets (namely IFN/ENIT Arabic for Arabic, IFN/Farsi for Persian, and George Washington for English). The results of experiments carried out on these three datasets show that the proposed method has much higher precision and recall than previous methods