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In the present paper, we used the Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) technique for English offline handwritten curve scripts and leads. Unlike other approaches, the PPTRPRT technique prioritizes segmentation of words and characters. The PPTRPRT technique extracts text regions from English offline handwritten cursive scripts and leads an iterative procedure for segmentation of text lines along with skew and de-skew operations. Iteration outcomes provide for pixel spacebased word segmentation which enables segmentation of characters. The PPTRPRT technique embraces various dispensations in segmentation of characters from English offline handwritten cursive scripts. Moreover, various normalization steps allow for deviations in pen breadth and inscription slant. Investigational outcomes show that the proposed technique is competent at extracting characters from English offline handwritten cursive scripts.

In the Indian subcontinent, a number of languages are in use, and an automatic recognition of printed and handwritten scripts facilitates number of applications such as image document sorting and penetrating online libraries of image documents. This framework proposed a bilingual (English and Hindi) character-spotting framework based on feedforward neural network which works on corpus of bilingual handwritten offline documents. The proposed Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) framework traced the actual text region of the offline handwritten bilingual scripts and lead the process of line segmentation along with skew and de-skew operations. The findings of the iterations were adopted in pixel-spacebased word segmentation, which were further used in character segmentation. Moreover, PPTRPRT performs normalization operation to incorporate all pen-breadth deviations and inscription slant. The proposed framework was state of the art, reflected clearly from the findings of the framework. The proposed framework is proficient to character segmentation and provides accuracy up to 99.78 %.

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