An experimental HMM-based postal OCR system

It is almost universally accepted in speech recognition that phone- or word-level segmentation prior to recognition is neither feasible nor desirable, and in the dynamic (pen-based) handwriting recognition domain the success of segmentation-free techniques points to the same conclusion. But in image-based handwriting recognition, this conclusion is far from being firmly established, and the results presented in this paper show that systems employing character-level presegmentation can be more effective, even within the same HMM paradigm, than systems relying on sliding window feature extraction. We describe two variants of a Hidden Markov system recognizing handwritten addresses on US mail, one with presegmentation and one without, and report results on the CEDAR data set. 1. INTRODUCTION Any approach to speech and handwriting recognition must take into account that the signal is composed from a succession of alphabetic units (phonemes or graphemes). In the early work on speech recog..

Raster to vector conversion: creating an unique handprint each time

When a person composes a document by hand, there is random variability in what is produced. That is, every letter is different from all others. If the person produces seven a s, none will be the same. This is not true when a computer prints something. When the computer produces seven a s they are all exactly the same. However, even with the variability inherent in a person s handwriting, when two people write something and they are compared side by side, they often appear as different as fonts from two computer families. In fact, if the two were intermixed to produce some text that has characters from each hand, it would not look right! The goal of this application is to improve the ability to digitally create testing materials (i. e., data collection documents) that give the appearance of being filled out manually (that is, by a person). We developed a set of capabilities that allow us to generate digital test decks using a raster database of handprinted characters, organized into hands (a single person s handprint). We wish to expand these capabilities using vector characters. The raster database has much utility to produce digital test deck materials. Vector characters, it is hoped, will allow greater control to morph the digital test data, within certain constraints. The long-term goal is to have a valid set of computer-generated hands that is virtually indistinguishable from characters created by a person

Accuracy improvement in odia zip code recognition technique

Odia is a very popular language in India which is used by more than 45 million people worldwide, especially in the eastern region of India. The proposed recognition schemes for foreign languages such as Roman, Japanese, Chinese and Arabic can’t be applied directly for odia language because of the different structure of odia script. Hence, this report deals with the recognition of odia numerals with taking care of the varying style of handwriting. The main purpose is to apply the recognition scheme for zip code extraction and number plate recognition. Here, two methods “gradient and curvature method” and “box-method approach” are used to calculate the features of the preprocessed scanned image document. Features from both the methods are used to train the artificial neural network by taking a large no of samples from each numeral. Enough testing samples are used and results from both the features are compared. Principal component analysis has been applied to reduce the dimension of the feature vector so as to help further processing. The features from box-method of an unknown numeral are correlated with that of the standard numerals. While using neural networks, the average recognition accuracy using gradient and curvature features and box-method features are found to be 93.2 and 88.1 respectively

Can the Archaeology of Manual Specialization Tell Us Anything About Language Evolution? A Survey of the State of Play

In this review and position paper we explore the neural substrates for manual specialization and their possible connection with language and speech. We focus on two contrasting hypotheses of the origins of language and manual specialization: the language-first scenario and the tool-use-first scenario. Each one makes specific predictions about hand-use in non-human primates, as well as about the necessity of an association between speech adaptations and population-level right-handedness in the archaeological and fossil records. The concept of handedness is reformulated for archaeologists in terms of manual role specialization, using Guiard's model asymmetric bimanual coordination. This focuses our attention on skilled bimanual tasks in which both upper limbs play complementary roles. We review work eliciting non-human primate hand preferences in co-ordinated bimanual tasks, and relevant archaeological data for estimating the presence or absence of a population-level bias to the right hand as the manipulator in extinct hominin species and in the early prehistory of our own species

Iris : a solution for executing handwritten code

This paper presents a novel approach to executing handwritten code, the solution coined Iris. My research falls within the field of mobile app development, handwriting recognition, optical and intelligent character recognition (OCR & ICR), machine learning, as well as various Computer Science-related fields such as domain specific languages, or DSLs. The solution outlined in this paper details a system where one can author code using only a writing utensil (such as a pen), scratch paper (such as a napkin), and a smart phone. Iris leverages the power of the cloud to process an image of handwritten code and return the result to the user. Ultimately, my results show that Iris was able to accurately execute handwritten scripts with various levels of observed accuracy. Future work includes adding more layers of machine learning as well as further pre-processing images prior to OCR

Rethinking Sustainability Towards a Regenerative Economy

This open access book is based on work from the COST Action “RESTORE - REthinking Sustainability TOwards a Regenerative Economy'', and highlights how sustainability in buildings, facilities and urban governance is crucial for a future that is socially just, ecologically restorative, and economically viable, for Europe and the whole planet. In light of the search for fair solutions to the climate crisis, the authors outline the urgency for the built environment sector to implement adaptation and mitigation strategies, as well as a just transition. As shown in the chapters, this can be done by applying a broader framework that enriches places, people, ecology, culture, and climate, at the core of the design task - with a particular emphasis on the benefits towards health and resilient business practices. This book is one step on the way to a paradigm shift towards restorative sustainability for new and existing buildings. The authors want to promote forward thinking and multidisciplinary knowledge, leading to solutions that celebrate the richness of design creativity. In this vision, cities of the future will enhance users’ experience, health and wellbeing inside and outside of buildings, while reconciling anthropic ecosystems and nature. A valuable resource for scientists and students in environmental sciences and architecture, as well as policy makers, practitioners and investors in urban and regional development

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

Computer analysis of composite documents with non-uniform background.

The motivation behind most of the applications of off-line text recognition is to convert data from conventional media into electronic media. Such applications are bank cheques, security documents and form processing. In this dissertation a document analysis system is presented to transfer gray level composite documents with complex backgrounds and poor illumination into electronic format that is suitable for efficient storage, retrieval and interpretation. The preprocessing stage for the document analysis system requires the conversion of a paper-based document to a digital bit-map representation after optical scanning followed by techniques of thresholding, skew detection, page segmentation and Optical Character Recognition (OCR). The system as a whole operates in a pipeline fashion where each stage or process passes its output to the next stage. The success of each stage guarantees that the operation of the system as a whole with no failures that may reduce the character recognition rate. By designing this document analysis system a new local bi-level threshold selection technique was developed for gray level composite document images with non-uniform background. The algorithm uses statistical and textural feature measures to obtain a feature vector for each pixel from a window of size (2 n + 1) x (2n + 1), where n ≥ 1. These features provide a local understanding of pixels from their neighbourhoods making it easier to classify each pixel into its proper class. A Multi-Layer Perceptron Neural Network is then used to classify each pixel value in the image. The results of thresholding are then passed to the block segmentation stage. The block segmentation technique developed is a feature-based method that uses a Neural Network classifier to automatically segment and classify the image contents into text and halftone images. Finally, the text blocks are passed into a Character Recognition (CR) system to transfer characters into an editable text format and the recognition results were compared to those obtained from a commercial OCR. The OCR system implemented uses pixel distribution as features extracted from different zones of the characters. A correlation classifier is used to recognize the characters. For the application of cheque processing, this system was used to read the special numerals of the optical barcode found in bank cheques. The OCR system uses a fuzzy descriptive feature extraction method with a correlation classifier to recognize these special numerals, which identify the bank institute and provides personal information about the account holder. The new local thresholding scheme was tested on a variety of composite document images with complex backgrounds. The results were very good compared to the results from commercial OCR software. This proposed thresholding technique is not limited to a specific application. It can be used on a variety of document images with complex backgrounds and can be implemented in any document analysis system provided that sufficient training is performed.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .A445. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1061. Advisers: Maher Sid-Ahmed; Majid Ahmadi. Thesis (Ph.D.)--University of Windsor (Canada), 2004