Recognizing Hand-Printed Letters and Digits
Authors: James A. Pittman Gale Martin
Published in: Advances in Neural Information Processing Systems 2  (NIPS 1989) (Main Conference Track), 1989

Abstract: We are developing a hand-printed character recognition system using a multi(cid:173) layered neural net trained through backpropagation.  We report on results of  training nets with samples of hand-printed digits scanned off of bank checks  and hand-printed letters interactively entered into a computer through a sty(cid:173) lus digitizer.  Given a large training set,  and a net with sufficient capacity to  achieve  high performance on the training set,  nets  typically achieved error  rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate.  The topology  and capacity of the system, as measured by the number of connections in the  net,  have  surprisingly little effect on generalization.  For those  developing  practical pattern recognition systems,  these results suggest that a large and  representative  training  sample  may be  the single,  most important factor in  achieving high recognition accuracy.  From a scientific standpoint, these re(cid:173) sults raise doubts about the relevance to backpropagation of learning models  that estimate the likelihood of high generalization from estimates of capacity.  Reducing capacity does have other benefits however, especially when the re(cid:173) duction is  accomplished by using local receptive fields  with shared weights.  In this latter case, we find the net evolves feature detectors resembling those  in visual cortex and Linsker's orientation-selective nodes. 
Practical interest in hand-printed character recognition is fueled by two current tech(cid:173) nology trends:  one toward systems that interpret hand-printing on hard-copy docu(cid:173) ments and one toward notebook-like computers that replace the keyboard with a stylus  digitizer.  The stylus enables users to write and draw directly on a flat panel display.  In this paper, we report on results applying multi-layered neural nets trained through  backpropagation (Rumelhart, Hinton, & Williams,  1986) to both cases. 
Developing pattern recognition systems is typically a two-stage process.  First, intuition  and experimentation are used to select a set of features to represent the raw input pat(cid:173) tern.  Then a variety of well-developed techniques are used to optimize the classifier  system that assumes this featural representation.  Most applications of backpropaga(cid:173) tion learning to character recognition use the learning capabilities only for this latter 
406  Martin and Pittman 
stage--developing the classifier system  (Burr, 1986; Denker, Gardner, Graf, Hender(cid:173) son, Howard, Hubbard, Jackel, Baird, & Guyon, 1989; Mori & Yokosawa, 1989; Weide(cid:173) man, Manry, &  Yau, 1989). However, backpropagation learning affords the opportuni(cid:173) ty  to optimize feature selection and pattern classification simultaneously. We avoid  using pre-determined features as input to the net in favor of using a pre- segmented,  size-normalized grayscale array for each character. This is a first step toward  the goal  of approximating the raw input projected onto the human retina, in that no pre-proces(cid:173) sing of the input is  required. 
We report on results for both hand-printed digits and letters.  The hand-printed digits  come from a set of 40,000 hand-printed digits scanned from the numeric amount region  of "real-world" bank checks.  They were  pre-segmented and  size-normalized  to a  15x24 grayscale array.  The test set consists of 4,000 samples and training sets varied  from 100 to 35,200 samples. Although it is always difficult to compare recognition rates  arising from different pattern sets, some appreciation for the difficulty of categoriza(cid:173) tion can be gained using human performance data as a benchmark.  An independent  person categorizing the test set of pre-segmented, size-normalized digits achieved an  error rate of 3.4%.  This figure is considerably below the near-perfect performance of  operators keying in numbers directly from bank checks, because the segmentation al(cid:173) gorithm is flawed. 
Working with  letters, as well as digits,  enables tests of the generality of results on a  different pattern set having more than double the number of output categories. The  hand-printed letters come from a set of 8,600 upper-case letters collected from over  110 people writing with a stylus input device on a flat panel display.  The stylUS  collects  a sequence of x-y coordinates at 200 points per second at a spatial resolution of 1000  points per inch.  The temporal sequence for each character is first converted to a size(cid:173) normalized bitmap array, keeping aspect ratio constant.  We have found that recogni(cid:173) tion accuracy is significantly improved if these bitmaps are blurred through convolution  with a gaussian distnbution.  Each pattern is represented as a 15x24 grayscale image.  A test set of 2,368 samples was extracted by selecting samples from  18 people, so that  training sets were generated by people different from  those generating the test set.  Training set sizes ranged from 500 to roughly 6,300 samples. 
1  HIGH  RECOGNITION  ACCURACY 
We find  relatively high  recognition accuracy for both pattern sets. Thble  11  reports  the minimal error rates achieved on the test samples for both pattern sets, at various  reject rates.  In the case of the hand-printed digits, the 4% error rate (0% rejects) ap-

Eff~cts of the number.of training samples and network capacity and  topology are reported in  the  next sectIon.  Nets were tramed to error rates of 2-3%.  1i"aining began with a learning rate of .05 and  a mome~tum value of .9.  The learning rate was decreased when  training accuracy began to oscillate or  had stabtlized for a large number of training epochs. We evaluate the output vector on a winner-take(cid:173) all  basis, as  this consistently improves accuracy and results in  network parameters having a smaller  effect on perfonnance. 

Recognizing Hand-Printed Letters and Digits 
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proaches the 3.4%  errors made by  the human judge.  This suggests that further im(cid:173) provements to generalization will require improving segmentation accuracy.  The fact  that an error rate of 5% was achieved for  letters is promising.  Accuracy is fairly high, 
Table 1:  Error rates of best nets trained on largest sample sets and tested