RAPID ANALYTICAL VERIFICATION OF HANDWRITTEN ALPHANUMERIC ADDRESS FIELDS

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF This paper presents a combination of fuzzy system and dynamic analytical model to deal with imprecise data derived from feature extraction in handwritten address images which are compared against postulated addresses for address verification. A dynamic building­number locator is able to locate and recognise the building­number, without knowing exactly where the building­number starts in the candidate address line. The overall system achieved a correct sorting rate of 72.9%, 27.1% rejection rate and 0.0% error rate on a blind test set of 450 cursive handwritten addresses.

Multi-experts for touching digit string recognition

84.6 % of touching digit strings have only two digits touching, 12.3 % have three digits touching, and 3.1% have more than three digits touching. We present a multiexperts approach to recognize touching digit pairs (TDP) and touching digit triples (TDT). We combine holistic and traditional segmentation methods. 25,686 TDP training samples and 2778 TDP testing samples collected from USPS mail are used in our experiment. Holistic method outperforms the traditional segmentation based methods. The multi-experts combination has the best performance, a correct rate of 91.1 % on TDP. 1

A Knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

"March 1994."Includes bibliographical references (p. [23]-[24]).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Karim Hussein ... [et al.

An Adaptive modular neural network with application to unconstrained character recognition

"August 1993."Includes bibliographical references (p. 24-27).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Lik Mui ... [et al.

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Novel word recognition and word spotting systems for offline Urdu handwriting

Word recognition for offline Arabic, Farsi and Urdu handwriting is a subject which has attained much attention in the OCR field. This thesis presents the implementations of offline Urdu Handwritten Word Recognition (HWR) and an Urdu word spotting technique. This thesis first introduces the creation of several offline CENPARMI Urdu databases. These databases were necessary for offline Urdu HWR experiments. The holistic-based recognition approach was followed for the Urdu HWR system. In this system, the basic pre-processing of images was performed. In the feature extraction phase, the gradient and structural features were extracted from greyscale and binary word images, respectively. This recognition system extracted 592 feature sets and these features helped in improving the recognition results. The system was trained and tested on 57 words. Overall, we achieved a 97 % accuracy rate for handwritten word recognition by using the SVM classifier. Our word spotting technique used the holistic HWR system for recognition purposes. This word spotting system consisted of two processes: the segmentation of handwritten connected components and diacritics from Urdu text lines and the word spotting algorithm. A small database of handwritten text pages was created for testing the word spotting system. This database consisted of texts from ten Urdu native speakers. The rule-based segmentation system was applied for segmentation (or extracting) for handwritten Urdu subwords or connected components from text lines. We achieved a 92% correct segmentation rate for 372 text lines. In the word spotting algorithm, the candidate words were generated from the segmented connected components. These candidate words were sent to the holistic HWR system, which extracted the features and tried to recognize each image as one of the 57 words. After classification, each image was sent to the verification/rejection phase, which helped in rejecting the maximum number of unseen (raw data) images. Overall, we achieved a 50% word spotting precision at a 70% recall rat

Algebraic fusion of multiple classifiers for handwritten digits recognition

Recognition of handwritten digits is a very popular application of machine learning. In this context, each of the ten digits (0-9) is defined as a class in the setting of machine learning based classification tasks. In general, popular learning methods , such as support vector machine, neural networks and K nearest neighbours, have been used for classifying instances of handwritten digits to one of the ten classes. However, due to the diversity of handwriting styles from different people, it can happen that some handwritten digits (e.g. 4 and 9) are very similar and are thus difficult to distinguish. Also, each single learning algorithm may have its own advantages and disadvantages, which means that a single algorithm would be capable of learning some but not all specific characteristics of handwritten digits. From this point of view, a method for handwritten digits recognition is proposed in the setting of ensemble learning, towards encouraging the diversity among different classifiers trained by different learning algorithms. In particular, the image features of handwritten digits are extracted by using the Convolutional Neural Network architecture. Furthermore, single classifiers trained respectively by K nearest neighbours and random forests are fused as an ensemble one. The experimental results show that the ensemble classifier was able to achieve a recognition accuracy of ≥ 98% using the MNISET data set