Abstract: A novel Binary Validation as Segmentation (BVS) is presented in this study. BVS is a bottom-up approach for character segmentation in off-line cursive handwriting recognition. The character segmentation is a process to extract individual character image from a handwritten word image. The extensive literature reveals that offline handwriting recognition still suffers from the absence of reliable character segmentation algorithms. The character segmentation stage is very crucial part in offline handwriting recognition because the recognition performance is directly affected by the segmentation performance. Therefore, improving the segmentation accuracy is very high prior task in this field. BVS takes over segmentation components to generate primitives. The over segmentation guarantees that none of primitives contains image pixels belonging to different characters. Based on the primitives, neighboring primitives are combined to create evaluation hypotheses. The most competent hypothesis is sought by global competency function using neural network classifier. BVS repeats cycles of combination and evaluation iteratively. Each cycle combines one a pair of two neighboring primitives permanently. That’s why the proposed approach includes the term, binary. BVS also introduces Continuous Foreground Transition (CFT) model to prevent under-segmentation errors. The proposed approach has been evaluated on CEDAR benchmark database. The results showed a significant improvement in segmentation errors. The analysis of results showed that the inclusion of CFT into the validation function has played a major role in improving over segmentation and bad-segmentation errors.