Text line detection is a critical step for applications in document image processing. In this paper, we propose a novel text
line detection method. First, the connected components are extracted from the image as symbols. Then, we estimate the
direction of the text line in multiple local regions. This estimation is, for the first time, to our knowledge, formulated
in a cost optimization framework. We also propose an efficient way to solve this optimization problem. Afterwards,
we consider symbols as nodes in a graph, and connect symbols based on the local text line direction estimation results.
Last, we detect the text lines by separating the graph into subgraphs according to the nodes’ connectivities. Preliminary
experimental results demonstrate that our proposed method is very robust to non-uniform skew within text lines, variability
of font sizes, and complex structures of layout. Our new method works well for documents captured with flat-bed and
sheet-fed scanners, mobile phone cameras, and with other general imaging assets.
The JBIG2 standard is widely used for binary document image compression primarily because it achieves much
higher compression ratios than conventional facsimile encoding standards, such as T.4, T.6, and T.82 (JBIG1).
A typical JBIG2 encoder works by first separating the document into connected components, or symbols. Next
it creates a dictionary by encoding a subset of symbols from the image, and finally it encodes all the remaining
symbols using the dictionary entries as a reference.
In this paper, we propose a novel method for measuring the distance between symbols based on a conditionalentropy
estimation (CEE) distance measure. The CEE distance measure is used to both index entries of the
dictionary and construct the dictionary. The advantage of the CEE distance measure, as compared to conventional
measures of symbol similarity, is that the CEE provides a much more accurate estimate of the number of
bits required to encode a symbol. In experiments on a variety of documents, we demonstrate that the incorporation
of the CEE distance measure results in approximately a 14% reduction in the overall bitrate of the JBIG2
encoded bitstream as compared to the best conventional dissimilarity measures.