Abstract
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.
Highlights
Whenever documents are manually scanned, a human-made orientation error almost always occurs, preventing the Optical Character Recognition (OCR) engine from properly detecting the scanned content
The proposed approaches range from projection profiling [6], neighborhood clustering [7,8], Hough transform applied on different selected key-points [9,10,11], Fast Fourier Transform (FFT) [12], Principal Component Analysis (PCA) [13], Radon transform [14], vertical projections [15], morphology [16], machine learning approaches [17], etc
They obtain robust and accurate results in most cases considering that the elements and properties they try to detect and measure are present in the input image
Summary
Whenever documents are manually scanned, a human-made orientation error almost always occurs, preventing the Optical Character Recognition (OCR) engine from properly detecting the scanned content. The proposed approaches range from projection profiling [6], neighborhood clustering [7,8], Hough transform applied on different selected key-points [9,10,11], Fast Fourier Transform (FFT) [12], Principal Component Analysis (PCA) [13], Radon transform [14], vertical projections [15], morphology [16], machine learning approaches [17], etc They obtain robust and accurate results in most cases considering that the elements and properties they try to detect and measure are present in the input image. Three skew detection algorithms were chosen to cover behavior and not just a general framework, theoretically-oriented, based on multiple experts’ decision, all the facets of the skew-related problems: the difficulty of finding the text orientation due to the alternative for classical approaches.
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