Abstract: The handwritten check recognition is a tough problem in pattern recognition.The challenge is that various handwriting styles and complex check backgrounds reduce the recognition accuracy.The capitalized Chinese currency amount is the most important part of a check, and its recognition is key to automatic processing of handwritten check images.This paper presents the study of segmentation-based recognition of handwritten capitalized Chinese currency amounts, and on this basis proposes a recognition method based on Convolutional Neural Network(CNN) and finite state automata.The method employs the over-segmented items and their combinations to obtain single characters, which are subsequently recognized by using CNN.Then the characters are categorized, and the logic relationships between them are defined to construct a finite state automaton for grammar detection.The automaton is used to select the grammatically correct strings from the recognition results, and the grammar automaton is used to optimize the performance of paths search.In addition, the grammar automaton is used to predict the fuzzy characters to correct the errors in the recognition results of CNN.The experimental results show that the accuracy of the proposed method achieves 98.2% for capitalized single characters, and 96.6% for text lines of currency amounts.
Convolutional Neural Network(CNN),
finite state automata,
handwritten bank check recognition,
capitalized Chinese currency amounts,
optical character recognition,