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1992, IEEE Computer
…
33 pages
1 file
A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail.>
Applied Intelligence, 2000
In this article, we describe the OCR and image processing algorithms used to read destination addresses from non-standard letters ( ats) by Siemens postal automation system currently in use by t h e Deutsche Post AG 1 .
2016
Computers excel at performing quantitative and repetitive tasks quickly, but they struggle with qualitative tasks, such as handwriting recognition. This is because it is effectively impossible to program recognition of millions of different handwriting styles into one algorithm. However, with the advent of machine learning, computers can be trained to recognize unknown objects and characters by analyzing a large labelled database. A practical use for machine learning is recognition of handwritten zip codes, which can be performed by a computer and image scanner much faster than a human can, in a setting such as a post office. In this project, the machine learning was performed within MATLAB using support vectors to define boundaries in multidimensional space between handwritten digits. The boundaries were based on a labelled dataset from MNIST (Mixed National Institute of Standards and Technology) containing 60,000 training images. Once the classification boundaries were formed, tes...
1988
A methodology for recognizing ZIP codes (US postal codes) in handwritten addresses is presented that uses many diverse pattern recognition and image processing algorithms. Given a high-resolution image of a handwritten address block, the solution invokes routines capable of hypothesizing the location of the ZIP code, segmenting and recognizing ZIP code digits, locating and recognizing city and state names, and looking up the results in a dictionary. The control structure is not strictly sequential, but rather in the form of a blackboard architecture that opportunistically invokes routines as needed. An implementation of the methodology is described as well as results with a database of grey-level images of handwritten addresses (taken from live mail in a US Postal Service mail processing facility). Future extensions of the approach are discussed
Computers & Electrical Engineering, 2013
In Iran like many other countries, the categorization of postal envelopes is executed manually, mostly based on the handwritten addresses and zip codes. That process is still slow and prone to man-made errors. Therefore, having an automated, accurate and efficient system to recognize handwritten zip codes is of high necessity for a faster and easier arrangement of postal envelopes, and consequently, enhanced performance of the post office. A complete system for Persian (Farsi) handwritten zip code detection and recognition is introduced in this paper. The proposed system consists of two phases; with zip code localization being the first and the main contribution of the study, and zip code digits recognition as the second. The first phase, proposes a state-of-the-art algorithm which localizes the zip code on the envelope. Subsequent to digit segmentation, handwriting zip code digits are recognized in the second phase, which includes feature extraction and classification sub-steps. The results obtained from the 50 test samples show an overall recognition rate of 92.9% in localization and recognition of handwritten zip codes.
International Journal of Scientific & Technology Research, 2020
In this paper we present an idea of using character recognition for sorting the handwritten postcards in postal service. The Indian postal letter sorting system has been made to recognize the postcards based on PIN codes by using Barcode approaches. The objective of the project is to provide an alternative mean to the traditional sorting system which consumes more time for processing and sorting the postcards based on their respective areas. It also aims at eliminating the human errors which may occurs during manual sorting. This can be achieved by character recognition for segregating the postcards based on their respective district. The character recognition process is carried out by taking the postcard images as input and the addresses are converted into string of pixel values. The string of pixel values is processed with the datasets. As a result, the characters are recognized for each input image. When the entire recognition process is completed, the addresses are sorted based ...
International Workshop on Frontiers in Handwriting Recognition, 2002
In integrated segmentation and recognition (ISR) of handwritten character strings, the underlying classifier is desired to be accurate in character classification and resistant to non-character patterns (also called garbage or outliers). This paper compares the performance of a number of statistical and neural classifiers in ISR. Each classifier has some variations depending on learning method: maximum likelihood estimation (MLE), discriminative learning (DL) under the minimum square error (MSE) or minimum classification error (MCE) criterion, or enhanced DL (EDL) with outlier samples. A heuristic pre-segmentation method is proposed to generate candidate cuts and character patterns. The methods were tested on the 5-digit Zip code images in CEDAR CDROM-1. The results show that training with outliers is crucial for neural classifiers in ISR. The best result was given by the learning quadratic discriminant function (LQDF) classifier.
In this paper, we present a system towards Indian postal automation. In the proposed system, at first, based on different characteristics of Indian postal documents Destination Address Block (DAB) is identified from the postal document. In India pincode numerals may be written within pin-code box or outside pin-code box. To extract such pin-code numerals, we propose a technique to take care the numerals written inside or outside the pin-code box. Since India is a multi-lingual and multi-script country, the address part may be written in two or more scripts. It is difficult to identify the script by which the pin-code numerals are written and hence we have used two-stage artificial Neural Network (NN) for the recognition of pin-code numerals written in English/Bangla.
Handwritten digits recognition has been widely studied because of its potential application in automatic sorting of mail pieces. In this paper, we focus on o¤-line isolated digits with unknown scriptor. TCSF/LER has developed an intermediate approach between classical methods, based on extracting small sets of parameters, and pure neural methods, in which the network is fed with raw image data. The proposed method combines image processing and connectionnist recognition. A vector of 90 parameters consisting in pro…le curves, measures of density and morphological information is computed from the digit image. Then a multilayer perceptron trained by backpropagation is used to classify. The method has been evaluated on a huge database of real zipcodes, provided by the SRTP Nantes. The database includes around 20000 digits for learning and 12000 digits for testing. The isolated digits come from pre…lled or free envelopes. Each digit has two labels provided by two operators: the …rst one sees the whole address block and the second one is restricted to seeing only the segmented digit. In this paper, we describe our approach and we give many experimental results: recognition rates on pre…lled envelopes, on free envelopes, on digits con…rmed by the second operator, etc.
In this paper, we present a system towards Indian postal automation based on PIN (Postal Index Number) code. Since India is a multilingual and multi-script country that was earlier colonized by UK, the address part may be written by combination of scripts such as Latin (English) and a local (state) script. Here, we shall consider Oriya script one of the local state language in India with English for recognition. It is very difficult to identify the script by which the PIN-code portion is written. So we have used two stage artificial neural network based general classifiers for the recognition of PIN-code digits written in Oriya. In this paper we propose a new technique namely, Quadrant-Mean technique to identify the numerals of PIN code written in Oriya script. By which the corresponding city name can be easily identified. The accuracy of the digit recognition module is 93.20%.
2008
Indian pin code is a six-digit string. Because of the writing style of different individuals some of the digits in a pin code string may touch with its neighboring digits. Accurate segmentation of such touching components into individual digits is a difficult task. To avoid such segmentation, here we consider a pin code string as word and the pin code recognition problem is treated as lexicon free word recognition. In the proposed method, at first, binarization of the input document is done. Next, water reservoir concept is applied to pre-segment a pin code string into possible primitive components (individual digits or its parts). Presegmented components of the pin code are then merged into possible digits to get the best pin code. In order to merge these primitive components into digits and to find optimum segmentation, dynamic programming (DP) is applied using total likelihood of digits as the objective function. To compute the likelihood of a digit, modified quadratic discriminant function (MQDF) is used. The features used in the MQDF are based on the directional information of the components. Our system on handwritten Bangla pin code shows 99.08% reliability when rejection and error rates are 19.28% and 0.74%, respectively.
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