Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Abstract

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 %.

Word Segmentation by Component Tracing and Association (CTA) Technique

“…Performance of CTA method has been compared with state-of-art techniques using DR, RA, and PM metrics as shown in table 1. Performance metrics for methods by Dahake et al (2017), Sharma and Dhaka (2016), Jain and Singh (2014), Karmakar et al (2014), andChaudhuri andPal (1997) for ICDAR2009 and ICDAR2013 datasets have been obtained from Sharma and Dhaka (2020). Performance of run length smoothing algorithm (RLSA) (Konidaris et al, 2007) has been obtained from its implementation by Gatos et al (2011).…”

Script-Independent Text Segmentation from Document Images

“…(Soora & Deshpande, 2018) calculated mean and standard deviation values of gap metrics to segment words. (Dhaka & Sharma, 2015;Sharma & Dhaka, 2016a,2016b…”

Offline Signature Authentication Algorithm Based on the Fuzzy Set

“…Otsu algorithm [5] establishes segmentation threshold according to the distribution of the gray histogram, which can quickly realize signature extraction. Continuity algorithm [34] extracts signatures one by one according to the continuity of signatures. EIT2FS algorithm [26] establishes a model with full consideration of noise interference based on the IT2FS.…”