Local connectivity feature transform of binary images containing text characters for optical character/word recognition

A method for processing a binary document image containing text characters, the method comprising:

• (a) obtaining the binary document image, the document image having a plurality of pixels, each pixel having either a first pixel value representing content of the document or a second pixel value representing background;
• (b) assigning the second pixel value to all pixels located on a boundary of the document image;
• (c) generating a transformed document image, the transformed document image being a grayscale image having a same size as the binary document image, including:
• (c1) for each pixel (i,j) of the document image that has the second pixel value, where i and j denote position indices of the document image respectively, assigning a fixed transform score to the pixel,
• (c2) for each pixel (i,j) of the document image that has the first pixel value, computing a transform score using
• T(i,j)=Σm=−1+1Σn=−1+1W(m,n)*P(i+m,j+n)
• where T(i,j) is the transform score of the pixel (i,j), m and n are integers and m, n∈{−1, 0, +1}, W(m,n) is a 3×3 weight matrix, and P(i+m,j+n) is the pixel value of pixel (i+m,j+n),
• wherein a center element of the 3×3 weight matrix W(m,n) has a value of zero, and each one of eight non-center elements of the 3×3 weight matrix W(m,n) has a value which is a different one of eight numbers 2q, q=0, 1, 2, . . . 7;
• and
• wherein the transform scores of all pixels of the document image form the transformed image;
• and
• (d) processing the transformed image using a bi-directional Long Short Term Memory (LSTM) neural network for character/word recognition to recognize characters or words in the transformed image.