State Estimation in a Document Image and Its Application in Text Block Identification and Text Line Extraction

Abstract

This paper proposes a new approach to the estimation of document states such as interline spacing and text line orientation, which facilitates a number of tasks in document image processing. The proposed method can be applied to spatially varying states as well as invariant ones, so that general cases including images of complex layout, camera-captured images, and handwritten ones can also be handled. Specifically, we find CCs (Connected Components) in a document image and assign a state to each of them. Then the states of CCs are estimated using an energy minimization framework, where the cost function is designed based on frequency domain analysis and minimized via graph-cuts. Using the estimated states, we also develop a new algorithm that performs text block identification and text line extraction. Roughly speaking, we can segment an image into text blocks by cutting the distant connections among the CCs (compared to the estimated interline spacing), and we can group the CCs into text lines using a bottom-up grouping along the estimated text line orientation. Experimental results on a variety of document images show that our method is efficient and provides promising results in several document image processing tasks.

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