Amit Jain
Aditya Kamat
Instructor Dr. Amitabha Mukerjee
 IIT Kanpur : April, 2000

Contents

Motivation

Handwriting is one of the easiest and natural ways of communication between humans and computers. Signature verification has been an active research topic for several decades in the image processing and signature verification remains a challenging issue. It provides a way of identifying the writer of a piece of handwriting in order to verify the claimed identity in security and related applications. It requires the writer to write the same fixed text. In the sense, signature verification may also be called text dependent writer verification. In practice, the requirement, the requirement and the use of fixed text makes writer verification it relatively less useful as it's not useful in many application. For example the identification of the writer of archived handwritten documents, crime suspect, identification in forensic science, etc. In these application, the writer of a piece of handwriting is often identified by professional handwriting examiners. Although human intervention in the text independent writer identification has been effective, it is costly and prone to fatigue. This task is much more difficult and useful than signature verification. So we got the motivation to do this challenging project.
 

Past Work

Papers have been published on this by H.E.S.Said. Not too much work has been done in this field as it's not a very easy one. We had studied several papers on Handwriting Analysis and Document Analysis and try to use a new approach for each phase.

Approach

 Research into writer identification has been focussed on two sterns, off-line and on-line writer identification.  Here We are working on the off-line writer identification. It is further sub divided into two parts Text Independent and Text Dependent, We are working on the former one as that is the more general case. This approach is called "Texture Analysis Technique".

The approach is basically based on the "Computer Image Processing and Pattern Recognition" techniques to solve the different phases of the problem- pre processing, feature extraction and selection of the correct writer. There are two approaches to off-line method, namely text dependent and text independent. We are working on text independent  approach where we are using a new texture analysis technique. This uses a feature set whose component describes global statistical feature extracted from the entire image of a text.

The algorithm is based on texture analysis and illustrated in figure. The three main stages are described below:


Methodology

As we shown above basically basically we can contain the whole work in three steps. We had described all the three phases as below.

Normalization of Handwriting Images

The input to this normalization stage is a binary image (pgm format ) of any handwritten document. The handwriting may contain lines of different point size and different spacing between lines, words and characters. The normalization is performed as follows;

Texture Analysis cannot be applied directly to input handwriting images, as texture is affected by different word spacing, varying line spacing etc. The influence of such factors is minimised by normalization. But before doing all this we first have to remove the noise from the image. For this first we do some type of thresholding with the image and then apply the Salt and Pepper filter to remove the noise. After applying this we will get the noise free image. Then further processing will be done on this image. This is subdivided into following steps.
 


LOCATING TEXT LINES

HORIZONTAL PROJECTION PROFILE

SKEW CORRECTION
SPACING BETWEEN WORDS/LINES

The spacing between lines and words could be varying that will change the feature so we had predefined these spacing so that the feature won't get changed.

PADDING

Padding is also required as this will also effect the feature set

Now we will choose the random non overlapping blocks from this pre-processed page from which the features will be extracted. We are choosing the randomized page as to improve the performance. After this step we will got a image with the specified spacing between lines and words and with the appropriate padding.
 

Feature Extraction

In principle, any texture analysis technique can be applied to extract features from each uniform block of handwriting. Here  we had  implement Gabor Filtering.

GABOR FILTER

The multi channel Gabor filtering technique is inspired by the psycho physical findings that the processing of pictorial information in the human visual cortex involves a set of parallel and quasi independent mechanisms or cortical channels which can be modeled by bandpass filters. Mathematically the 2D Gabor filter can be expressed as follows:


where g(x,y) is a 2-D Gaussian function of the form

Each cortical channel is modeled by a pair of Gabor filters

We will convolve the input image with these two filters and then we will got two iamges say qe(x,y) and qo(x,y) then by applying the following formula we will got the final image as a gabor output for a particular value of f and theta.

The two important parameters of Gabor filter are f (the radial frequency) and theta (orientation), which define the location of the channel in the frequency plane. Commonly used frequencies are power of 2. It is known that for any image of size NxN, the important frequency components are within f<=N/4 cycles/degree.

In this we will use frequencies of 4,8,16,32 cycles/degree. For each central frequency f, filtering is performed at an angle of 00, 450, 900, 1350. This gives a total of 16 output images (four for each frequency) from which the writer's features are extracted.

Writer Identification

We will require a database to start with. So for this we will first define our domain of writers then from each of these writer we will take 10-15  document and then we will maintain a database of features. And we will do this for all of the member of our database as below is a image that contain handwriting of 10 different people.

There are essentially two classifiers, namely the Weighted Euclidean Distance (WED) classifier and the K-Nearest neighbor classifier (K-NN). But here we are using the WED approach.
 
 

THE WEIGHTED EUCLIDEAN DISTANCE CLASSIFIER

Representative features  for each writer are determined from the features extracted from training handwriting texts of the writer. Similar feature extraction operations are carried out, for an input handwritten text block by an unknown writer. The extracted features are then compared with the representative features of a set of known writers. The writer of the handwriting is identified as writer K by the WED classifier if the following distance function is a minimum at K:


where fn is the nth feature of the input document, and nf(k) and nv(k) are the sample mean and the sample standard deviation of the nth feature of writer k respectively.

Input and Output

The Input to our Program will be a Image (pgm format ) of a document written by a person. Basically this will be a scanned image of the document then we  will apply our given methodology as described above then we will get a table of features and from this table and with our database

Then we will conclude who is the writer of this document and also that whether he/she belong to our database domain or not. So the output of our software will be the name of the writer or just an error signal saying that It doesn't belong to our domain.

Example:

We will illustruate a example here. By which whole the methodology will become more clearer.
Here we had taken a input image as shown in the figure. This is the scanned image of a writer. First we had converted it into pgm format to do processing with it.
 

Input Image
 
Now  we will do Normalisation with this. As we had described in our approach this is necessary as we can only extract features from some standard format as spacing between words and line of reference, etc.

To do pre-processing as described in the Methodology we will follow each step one by one. First we will change the value of each pixel either 0 or 255 by applying some thresholding so that we can call each pixel either WHITE or BLACK. Then we had applied Salt and Paper Filter to remove the noise from the page. After doing all this we will get the image as shown below.
 

After Noise Removal

Now the next step is to calculate the skew angle and to rotate the image by this angle. So for this we will follow the steps described above. By doing all this we will get the skew angle =3o and now we will rotate the image by 3o. And we had got the image as shown below.
 
 

After Rotation

Now we have to do the correct the interword and interline spacing and after this we have to do padding. we had also set some pre-defined Left and Right Margin and the Top and Bottom margin. After doing all these we will got the output as shown below. By doing all these the output got compressed.
 

This is the Image After Preprocessing

Now  from this image on applying the Feature Extraction Techniques we will get the images as showed in the figure these are on different frequencies and for different angle that we will use in Gabor Filter. So for this image we will get 16 different images and 32 different features (2 for each).

Now from these features we will conclude the identity of the writer using the writer Identification approach as described above. We will chose the one which have the minimum Euclidean distance.

Results

We trained our software with images of handwritten documents for 9 writers with 9-10 pages per writer and maintained a database. With this system we  now tried  various test documents (these documents were not used during training).  Out of the 32 test images that we used, the system gave the correct results in 24 cases. This implies that the system was able to identify the writer in about 75% of the cases.

Conclusions

Texture based methods like Gabor filtering can be successfully applied for handwriting recognition by choosing the right parameter set. In our case we selected radial frequency values 4,8,16 and 32 with orientation angles from 0 to 135 degrees with increments of 45. The objective  here is to get feature clusters which are as far apart as possible. For our purpose, the values we selected seem to give fairly accurate results. However, for texture based methods to be applied, a good amount of preprocessing is required. The algorithms that we have used in the preprocessing stage have been culled out from several sources. Several alternate methodologies exist for such preprocessing. We selected those which give the best results  (like Horizontal Projection Profile).  Lastly, even in the writer identification stage, we used the method of Weighted Euclidean Distance (WED) over k-NN (k- Nearest Neighbor) as earlier works show that WED gives better results as compared to k-NN.

In conclusion, at every stage of the implementation, we have adopted methods which we think best suit our purpose making certain changes in the algorithms and testing the same to get as optimal results as possible.

Further Improvement

Basically the performance of the system will depend on the quantity and quality of input data. Here due to insufficient data we had trained our system with 10 pages per writer. If we can train it with say 20 or more than performance will change drastically. Another  points are that we had taken our data from the copies of the students, there was a lot of noise like cutting of line etc. that can't be handle with the software. And the writer in this case has also used the different pen In this way the features will change. And with in the same page he may have wrote at two different angles that will also create a problem. So the performance of the system will go down.

So for better results we should train our system with more input data and the pen size should be same, and the noise should me minimum.

We are thinking of to use the Neural Network to train the system But due to insufficient data we haven't used this approach. By using Neural Networks we can improve the performance.

On-Line Links

@Article{H.E.S.Said/T.N.Tan/K.D.Baker:1998,
  author=          { H.E.S. Said, T.N. Tan and K.D.Baker},
  keywords=     { Personal identification    Writer identification    Textutr Analysis    Handwriting processing},
  institution=   { Unioversity of Reading, Whiteknights},
  title=               {Personal Identification based on Handwriting},
  journal=         {PATTERN RECOGNITION, The journal of Pattern Recognition Society},
  month=          {January},
  year=               {2000},
  volume=         {33},
  pages=            {149-160},
  e-mail=          {H.E.S.Said@reading.ac.uk},
  annote=          {

There are many techniques for handwriting based writer identification. Basically there are two technique one is text dependent and another is text independent. In the first one we have to write the same fixed text for the verification but that is not what we need in real life applications like we want to recognize a criminal on this basis than we need text independent writer identification. In this paper they had tried to remove this text dependency.

Assuming that handwriting of different people is often visually distinctive, this paper present an off line system based image processing and pattern recognition techniques and use text independent approach where texture analysis use the global statistical feature extracted from the entire image of the text. It uses the Gabor filter for filtration. To remove spacing, varying line spacing we use Normalizing then use Gabor filter extract feature from the image then finally we compare the results from the data base in order to recognize the writer.

The performance of this basically depends on the efficiency of the three parts (figure). So we can think of the better way of Normalizing. And there is also a loose point of assuming the fact that handwriting of different people is visually distinct but this is not always the case.

The author deserve credit for taking into account the point which have been ignored in previous approaches. His method takes care that the approach is text/context independent. The paper is not very technical and mathematical which makes it quite easy  for a layman to understand but he should know Gabor filter, GSCM, WED and K-NN. This field is very interesting and have a lot of room for research.

                                    Amit Jain, 1/2000},
}
 

This proposal is prepared by Amit Jain and Aditiya Kamat as a part of the project component in the Course on Artificial Intelligence in Engineering in Janurary  IInd semester of 2000 under Dr. Amitabha Mukerjee

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