Voting-Based Document Image Skew Detection

Optical Character Recognition (OCR) is an indispensable tool for technology users nowadays, as our natural language is presented through text. We live under the need of having information at hand in every circumstance and, at the same time, having machines understand visual content and thus enable the user to be able to search through large quantities of text. To detect textual information and page layout in an image page, the latter must be properly oriented. This is the problem of the so-called document deskew, i.e., finding the skew angle and rotating by its opposite. This paper presents an original approach which combines various algorithms that solve the skew detection problem, with the purpose of always having at least one to compensate for the others’ shortcomings, so that any type of input document can be processed with good precision and solid confidence in the output result. The tests performed proved that the proposed solution is very robust and accurate, thus being suitable for large scale digitization projects.

Segmentation of Characters in a Handwritten Kannada Document

Purpose: The main purpose of the proposed research work is to perform the segmentation of characters from the handwritten Kannada document. The reason behind segmentation is to support the implementation of handwriting recognition system for Kannada language. Methodology: To perform segmentation of characters, input document has to go through gray scale conversion, denoising, contrast normalization and binarization process. Result: Documents collected from ICDAR-2013 and ICDAR-2015 were considered for experiment and obtained 100% accuracy for line segmentation and 96% accuracy for character segmentation. Conclusion:: To further improve the efficiency with respect to accuracy of character segmentation, other pre-processing steps like skew detection and correction shall be considered.