Postal Address Block Location Using A 
Convolutional Locator Network 
Ralph Wolf and John C. Platt 
Synaptics, Inc. 
2698 Orchard Parkway 
San Jose, CA 95134 
Abstract 
This paper describes the use of a convolutional neural network 
to perform address block location on machine-printed mail pieces. 
Locating the address block is a difficult object recognition problem 
because there is often a large amount of extraneous printing on a 
mail piece and because address blocks vary dramatically in size and 
shape. 
We used a convolutional locator network with four outputs, each 
trained to find a different corner of the address block. A simple 
set of rules was used to generate ABL candidates from the network 
output. The system performs very well: when allowed five guesses, 
the network will tightly bound the address delivery information in 
98.2% of the cases. 
1 INTRODUCTION 
The U.S. Postal Service delivers about 350 milhon mail pieces a day. On this scale, 
even highly sophisticated and custom-built sorting equipment quickly pays for itseft. 
Ideally, such equipment would be able to perform optical character recognition 
(OCR) over an image of the entire mail piece. However, such large-scale OCR is 
impractical given that the sorting equipment must recognize addresses on 18 mail 
pieces a second. Also, the large amount of advertising and other irrelevant text that 
can be found on some mail pieces could easily confuse or overwhelm the address 
recognition system. For both of these reasons, character recognition must occur 
745 
746 Wolf and Platt 
Figure 1: Typical address blocks from our data set. Notice the wide variety in 
the shape, size, justification and number of lines of text. Also notice the detached 
ZIP code in the upper right example. Note: The USPS requires us to preserve the 
confidentiality of the mail stream. Therefore, the name fields of all address block 
figures in this paper have been scrambled for publication. However, the network 
was trained and tested using unmodified images. 
only on the relevant portion of the envelope: the destination address block. The 
system thus requires an address block location (ABL) module, which draws a tight 
bounding box around the destination address block. 
The ABL problem is a challenging object recognition task because address blocks 
vary considerably in their size and shape (see figure 1). In addition, figures 2 and 3 
show that there is often a great deal of advertising or other information on the mail 
piece which the network must learn to ignore. 
Conventional systems perform ABL in two steps (Caviglione, 1990) (Palumbo, 
1990). First, low-level features, such as blobs of ink, are extracted from the im- 
age. Then, address block candidates are generated using complex rules. Typically, 
there are hundreds of rules and tens of thousands of lines of code. 
The architecture of our ABL system is very different from conventional systems. 
Instead of using low-level features, we train a neural network to find high-level 
abstract features of an address block. In particular, our neural network detects 
the corners of the bounding box of the address block. By finding abstract features 
instead of trying to detect the whole address block in one step, we build a large 
degree of scale and shape invariance into the system. By using a neural network, 
we do not need to develop explicit rules or models of address blocks, which yields a 
more accurate system. 
Because the features are high-level, it becomes easy to combine these features into 
object hypotheses. We use simple address block statistics to convert the corner 
features into object hypotheses, using only 200 lines of code. 
Postal Address Block Location Using a Convolutional Locator Network 747 
2 SYSTEM ARCHITECTURE 
Our ABL system takes 300 dpi grey scale images as input and produces a list of the 
5 most likely ABL candidates as output. The system consists of three parts: the 
preprocessor, a convolutional locator network, and a candidate generator. 
2.1 PREPROCESSOR 
The preprocessor serves two purposes. First, it substantially reduces the resolution 
of the input image, therefore decreasing the computational requirements of the 
neural network. Second, the preprocessor enhances spatial frequencies in the image 
which are associated with address text. The recipe used for the preprocessing is as 
follows: 
1: Clip the top 20, of the image. 
2: Spatgaily filter with a passband of 0.3 to 1.4min. 
3: Take the absolute value of each pixel. 
4: Low-pass filter and subsample by a factor of 16 in X and Y. 
5: Perform a linear contrast stretch, mapping the darkest 
pixel to 1.0 and the lightest pixel to 0.0. 
The effect of this preprocessing can be seen in figures 2 and 3. 
2.2 CONVOLUTIONAL LOCATOR NETWORK 
We use a convolutional locator network (CLN) to find the corners of the bounding 
box. Each layer of a CLN convolves its weight pattern in two dimensions over the 
outputs of the previous layer (LeCun, 1989) (Fukushima, 1980). Unlike standard 
convolutional networks, the output of a CLN is a set of images, in which regions 
of activity correspond to recognition of a particular object. We train an output 
neuron of a CLN to be on when the receptive field of that neuron is over an object 
or feature, and off everywhere else. 
CLNs have been previously used to assist in the segmentation step for optical charac- 
ter recognition, where a neuron is trained to turn on in the center of every character, 
regardless of the identity of the character (Martin, 1992) (Platt, 1992). The recogni- 
tion of an address block is a significantly more difficult image segmentation problem 
because address blocks vary over a much wider range than printed characters (see 
figure 1). 
The output of the CLN is a set of four feature maps, each corresponding to one 
corner of the address block. The intensity of a pixel in a given feature map represents 
the likelihood that the corresponding corner of the address block is located at that 
pixel. 
Figure 4 shows the architecture of our convolutional locator network (CLN). It has 
three layers of trainable weights, with a total of 22,800 free parameters. The network 
was trained via weight-shared backpropagation. The network was trained for 23 
epochs on 800 mail piece images. This required 125 hours of cpu-time on an i860 
based computer. Cross validation and final testing was done with two additional 
748 Wolf and Platt 
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Postal Address Block Location Using a Convolutional Locator Network 749 
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Figure 3: Another example from the test set. The preprocessed image still has a 
large amount of background noise. In this example, the first candidate of the ABL 
system (shown in the lower left) was almost correct, but the ZIP code got truncated. 
The second candidate of he system (shown in the lower right) gives the complete 
address. 
750 Wolf and Platt 
Third layer of weights 
4 36x16 windows 
Second layer of weights 
8 9x9 windows 
First layer of weights 
6 9x9 windows 
Output maps 
Second layer feature maps 
2x2 subsampled first layer 
feature maps 
First layer feature maps 
Input image 
Figure 4: The architecture of the convolutional locator network used in our ABL 
system. 
data sets of 500 mail pieces each. All together, these 1800 images represent 6 Gbytes 
of raw data, or 25 Mbytes of preprocessed images. 
2.3 CANDIDATE GENERATOR 
The candidate generator uses the following recipe to convert the output maps of 
the CLN into a list of ABL candidates: 
1: Find the top 10 local maxima in each feature map. 
2: Construct all possible ABL candidates by combining pairs 
local maxima Irom opposing corners. 
3: Discard candidates which have negative length or width. 
4: Compute conlidence ol each candidate. 
6: Sort the candidates according to conlidence. 
6: Remove duplicate and near duplicate candidates. 
7: Pad the candidates by a lixed amount on all sides. 
The confidence of an address block candidate is: 
Caddress block -- Psizeocation H C/ 
i=1 
where Caddr block is the confidence of the address block candidate, rsie is the 
prior probability of finding an address block of the hypothesized size, /ocation is 
the prior probability of finding an address block in the hypothesized location, and 
Postal Address Block Location Using a Convolutional Locator Network 751 
C'i are the value of each of the output maxima. The prior probabilities Psie and 
Pocation were based on smoothed histograms generated from the training set and 
validation set truths. 
Steps 6 and 7 each contain 4 tuning parameters which we optimized using the 
validation set and then froze before evaluating the final test set. 
3 SYSTEM PERFORMANCE 
Figures 2 and 3 show the performance of the system on two challenging mail pieces 
from the final test set. We examined and classified the response of the system to all 
500 test images. When allowed to produce five candidates, the ABL system found 
98.2% of the address blocks in the test images. 
More specifically, 96% of the images have a compact bounding box for the complete 
address block. Another 2.2% have bounding boxes which contain all of the delivery 
information, but omit part of the name field. The remaining 1.8% fail, either 
because none of the candidates contain all the delivery information, or because 
they contain too much non-address information. The average number of candidates 
required to find a compact bounding box is only 1.4. 
4 DISCUSSION 
This paper demonstrates that using a CLN to find abstract features of an object, 
rather than locating the entire object, provides a reasonable amount of insensitivity 
to the shape and scale of the obj.ect. In particular, the completely identified address 
blocks in the final test set had aspect ratios which ranged from 1.3 to 6.1 and their 
absolute X and Y dimensions both varied over a 3:1 range. They contained anywhere 
from 2 to 6 lines of text. 
In the past, rule-based systems for object recognition 'were designed from scratch 
and required a great deal of domain-specific knowledge. CLNs can be trained to 
recognize different classes of objects without a lot of domain-specific knowledge. 
Therefore, CLNs are a general purpose object segmentation and recognition archi- 
tecture. 
The basic computation of a CLN is a high-speed convolution, which can be cost- 
effectively implemented by using parallel hardware (Sickinger, 1992). Therefore, 
CLNs can be used to reduce the complexity and cost of hardware recognition sys- 
tems. 
5 CONCLUSIONS 
In this paper, we have described a software implementation for an address block 
location system which uses a convolutional locator network to detect the corners of 
the destination address on machine printed mail pieces. 
The success of this system suggests a general approach to object recognition tasks 
where the objects vary considerably in size and shape. We suggest the following 
752 Wolf and Platt 
three-step approach: use a simple preprocessing algorithm to enhance stimuli which 
are correlated to the object, use a CLN to detect abstract features of the objects in 
the preprocessed image, and construct object hypotheses by a simple analysis of the 
network output. The use of CLNs to detect abstract features enables versatile object 
recognition architectures with a reasonable amount of scale and shape invariance. 
Acknowledgement s 
This work was funded by USPS Contract No. 104230-90-C-3441. The authors would 
like to thank Dr. Binh Phan of the USPS for his generous advice and encourage- 
ment. The images used in this work were provided by the USPS. 
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