problem to recognize a table through a png image

Here's a way to augment @georg279's approach. We can use ImageLines to subdivide the image.

First we use a derivative filter to highlight the horizontal lines. The parameters need to be tweaked by hand.

img = Import@"http://i.stack.imgur.com/Ricz2.png"
i2 = Binarize[DerivativeFilter[img, {1, 0}, 0.2], 0.09]

Mathematica graphics

Then we can get the lines and inspect them:

lines = ImageLines[i2, 0.19, 0.008];
HighlightImage[img, {Orange, Line /@ lines}]

Mathematica graphics

We got every row entry, plus a block below the table, which we can discard later. We can use the coordinates in lines to subdivide the image and apply TextRecognize to the pieces:

tdata = TextRecognize[ImageResize[#, Scaled[8]]] & /@ 
  Reverse@Rest[
    ImageTake[img, -Reverse@#] & /@ 
     Partition[Round@Sort@lines[[All, 1, 2]], 2, 1]]

We can then convert the numerals in the last ten columns to numeric data. There's a problem with the missing data in the columns and the spaces in the names in the first column. By padding with "XXX", the entries last column were all converted, but removing the Xs took inspection.

Replace[
 ToExpression[(StringSplit[tdata] /. 
     "X" | "XX" | "XXX" | "xxx" :> Sequence[])[[All, -11 ;;]]],
 {x_Real :> x, n_Integer :> n, I | $Failed -> Missing["NotAvailable"]},
 2]
(*
  {{2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013},
   {1.76, 1.76, 1.78, 1.78, 1.85, 1.94, 1.93, 1.97, 2.01, 2.01}, 
   ...
   {2.49, 2.68, 2.79, 3.01, 3.21, 3.36, 3.56, 3.74, 4.04, 
    Missing["NotAvailable"], Missing["NotAvailable"]},
   {2.55, 2.49, 2.51, 2.55, 2.63, 2.77, 2.82, 2.74, 2.77, 2.81, Missing["NotAvailable"]}}
*)

TextRecognise seems to fare better if you feed it smaller regions. Here I manually isolate individual lines from the table:

i0 = Import["http://i.stack.imgur.com/Ricz2.png"];
ImageTake[i0, {60, 73}]

enter image description here

Column[TextRecognize[
       ImageResize[ImageTake[i0, {#, # + 13}], Scaled[8]]] & /@ 
          Range[60, 360, 16]]

enter image description here