中文摘要：

This paper presents a method for locating text based on a simplified pulse coupled neural network (PCNN).The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing.The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector.The different edges are integrated using an automatically adjusted weighting coefficient.Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system.This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem.The algorithm is a simplified PCNN edge-based (PCNNE) algorithm.Three comparison tests are used to evaluate the algorithm.Tests on large data sets show PCNNE efficiently detects texts with various colors,font sizes,positions,and uneven illumination.This method outperforms several traditional methods both in text detection rate and text detection accuracy.

英文摘要：

This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based （PCNNE） algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.