7 research outputs found
Characters Segmentation of Cursive Handwritten Words based on Contour Analysis and Neural Network Validation
This paper presents a robust algorithm to identify the letter boundaries in images of unconstrained handwritten word . The proposed algorithm is based on vertical contour analysis. Proposed algorithm is performed to generate presegmentation by analyzing the vertical contours from right to left. The unwanted segmentation points are reduced using neural network validation to improve accuracy of segmentation. The neural network is utilized to validate segmentation points. The experiments are performed on the IAM benchmark database. The results are showing that the proposed algorithm capable to accurately locating the letter boundaries for unconstrained handwritten words
Analysis of Segmentation Performance on the CEDAR Benchmark Database
The purpose of this paper is to analyse the performance of our improved segmentation algorithm tested on the CEDAR benchmark database. Segmentation is achieved through the extraction of a wide range of information adjacent to or surrounding suspicious segmentation points. Initially, a heuristic technique is employed to search for structural features and to over-segment each word. For each segmentation point that is located, the left character (preceding the segmentation point), and centre character (centred on the segmentation point) are extracted along with other features from the segmentation area. The aforementioned features are presented to trained character and segmentation point validation neural networks to evaluate a number of confidence values. Finally, the confidence values are fused to obtain the final segmentation decision. Based on a detailed analysis, it was observed that the left and centre character networks increased the accuracy of the segmentation algorithm. 1