Abstract Sense‐making, sensemaking, and sense making are terms used in different disciplines. Similarities of usage seem unclear. (1) to examine the concepts used in different approaches to sense‐making/sensemaking/sense making; (2) to identify, classify and synthesize recent studies relevant to information science, as well as similar group on sensemaking in education research; (3) to reflect on future directions for sense‐making/sensemaking methodology in information science. The objectives were to retrieve, examine, classify and perform meta‐synthesis on sense‐making/sensemaking studies in both information science and education research. The review used systematic review principles, with selection criteria for case studies for examination in both information science and education sets. The final meta‐synthesis used a meta‐ethnographic approach, together with findings of recent overviews on organizational sensemaking, and other information science reviews. Qualitative sense‐making studies in information science often used Dervin's SMM (sense‐making methodology) and studies in organizations and education frequently used Weick's organizational sensemaking. Different mixed methods approaches were identified. Sense‐making is actively used in research and practice in information science and knowledge management. Using a coherent sense‐making methodology helps and dialogic principles are useful in planning, data collection and analysis. Individual and collective sense‐making are important to information science.

This paper introduces new handwritten databases of selected words in the five Middle-Eastern languages of Arabic, Dari, Farsi, Pashto and Urdu. The databases share a common lexicon of forty words that are related to finance and are used in daily life. The five databases have been collected from over 1600 native writers located in four countries. Recognition results for each of the databases are also presented. Results come from three classifiers (Support Vector Machines, Modified Quadratic Discriminant Function. And Multi-layer Perceptron) which were implemented for recognition of the words based on gradient features. Given the diversity of the data, the results demonstrate the effectiveness of the implemented process in learning and recognizing samples of handwritten words from different languages. In addition, full page handwritten documents of each language are presented, with approximately forty pages per language. Each document has associated ground truth information.

Large amounts of handwritten documents have been digitized, and the need to search and index these documents is increasing to make them more accessible. Different word spotting systems have been proposed to search for words for this purpose. Since the precision of the word spotting system is crucial, verifying the results of a word spotting system is becoming an effective approach to improve the system performance. In this paper, we propose two verification models for Arabic word spotting systems. Both models make use of a holistic classifier. The first model is based on matching the results of the word spotting system with those of the holistic classifier, while the other model derives a new score evaluation based on the two results. Verifying a word spotting system using these models can significantly improve its performance, since the precision rate increased from 74% to 77.7% and 84.4% respectively with the Word Matching and Score Evaluation models of verification, at 50% recall.

This paper presents a robust method for handwritten text line extraction. We use morphological dilation with a dynamic adaptive mask for line extraction. Line separation occurs because of the repulsion and attraction between connected components. The characteristics of the Arabic script are considered to ensure a high performance of the algorithm. Our method is evaluated on the CENPARMI Arabic handwritten documents database which contains multi-skewed and touching lines. With a matching score of 0.95, our method achieved precision and recall rates of 96:3% and 96:7% respectively, which demonstrate the effectiveness of our approach.

With the ever-increasing amounts of published materials being made available, developing efficient means of locating target items has become a subject of significant interest. Among the approaches adopted for this purpose is word spotting, which enables the identification of documents through the use of pertinent keywords. This paper reports on an effective method of word spotting for Arabic handwritten documents that takes into consideration the nature of Arabic handwriting. Parts of Arabic Words (PAWs) form the basic components of this search process, and a hierarchical classifier (consisting of a set of classifiers each trained on a different part of the input pattern) is implemented. For the first time in Arabic word spotting, language models are incorporated into the process of reconstructing words from PAWs. Details of the method and promising experimental results are also presented.

In order to spot the digits in a handwritten document, each component is sent to a classifier. This is a time consuming process because a document usually contains several hundred components. A method is presented to reduce the number of candidate components from a handwritten document sent to the classifier. Furthermore, since the classifier does not contain a rejection class, this led to several misclassifications. To lessen this, a verification post processing module was developed in order to reject some false positives. We reached an overall precision of 80% and 83.3% recall on our test set of handwritten documents.

In handwriting recognition, confusing/conflicting writing styles can result in irreducible errors, so the study of writing style consistencies is important for applications. In Arabic Handwritten Numeral Recognition, most errors occur between samples of classes two and three due to their very similar shapes in some writing styles. In this paper, an automated writing style detection process is effectively implemented in the pair-wise verification of samples in these two classes. As a result, the recognition results have improved significantly with a reduction by 25% of previous errors. With rejection, when the LDA (Linear Discriminant Analysis) measurement rejection threshold is adjusted to maintain the same error rate, the recognition rate increases from 96.87% to 97.81%.

Document Script Identification (DSI) is a very useful application in document processing. This paper presents a method for this application that uses a new noise tolerant feature, the Downgraded Pixel Density feature. Compared to other features widely used in existing DSI solutions, this new feature is much more robust to variations in slant, font and style of printed documents. Experimental results show that the method achieves promising identification performances.

This paper presents a linear discriminant analysis based measurement (LDAM) on the output from classifiers as a criterion to reject the patterns which cannot be classified with high reliability. This is important in applications (such as in processing of financial documents) where errors can be very costly and therefore less tolerable than rejections. To implement the rejection, which can be considered to be a two-class problem of accepting the classification result or otherwise, Linear discriminant analysis (LDA) is used to determine the rejection threshold at a new approach. LDAM is designed to take into consideration the confidence values of the classifier outputs & the relations between them, and it is an improvement over traditional rejection measurements such as first rank measurement (FRM) and first two ranks measurement (FTRM). Experiments are conducted on the CENPARMI Arabic isolated numerals database. The results show that LDAM is more effective, and it can achieve a higher reliability while achieving a high recognition rate.

This paper describes a system being developed to recognizedate information handwritten on Canadian bankcheques. A segmentation based strategy is adopted in thissystem. In order to achieve high performances in terms ofefficiency and reliability, a knowledge-based module is proposedfor the date segmentation and a cursive month wordrecognition module is implemented based on a combinationof classifiers. The interaction between the segmentation andrecognition stages is properly established by using multi-hypothesesgeneration and evaluation modules. As a result,promising performance is obtained on a test set from a real-lifestandard cheque database.

Segmenting handwritten date fields on bank cheque images into three subimages corresponding to the day, month and year is the first and critical step of our date recognition system. The paper describes a knowledge-based segmentation system, which introduces different kinds of knowledge at different segmentation stages to improve the performance. The knowledge includes information on the writing style, syntactic and semantic constraints, etc. Results have shown that the system is very effective compared with a previous structural feature based method.

The last frontiers of handwriting recognition are considered to have started in the last decade of the second millennium. The paper summarizes (a) the nature of the problem of handwriting recognition, (b) the state of the art of handwriting recognition at the turn of the new millennium, and (c) the results of CENPARMI researchers in automatic recognition of handwritten digits, touching numerals, cursive scripts, and dates formed by a mixture of the former 3 categories. Wherever possible, comparable results have been tabulated according to techniques used, databases, and performance. Aspects related to human generation and perception of handwriting are discussed. The extraction and usage of human knowledge, and their incorporation into handwriting recognition systems are presented. Challenges, aims, trends, efforts and possible rewards, and suggestions for future investigations are also included.

Two types of techniques are usually adopted in language differentiation: token matching and statistical analysis. In this paper we present a method which uses a combined analysis of several discriminating statistical features, for the differentiation between European and oriental language scripts. When applied to more than 23 languages, it has proved to be effective in differentiating between documents printed in these different scripts.

Two types of techniques are usually adopted in language differentiation: token matching and statistical analysis. In this paper we present a method which uses a combined analysis of several discriminating statistical features for the differentiation between European and oriental language scripts. When applied to more than 23 languages, it has proved to be effective in classifying documents printed in these different scripts.

It has been demonstrated that combining the decisions of several classifiers can lead to better recognition results. The combination can be implemented using a variety of strategies, among which majority vote is by far the simplest, and yet it has been found to be just as effective as more complicated schemes in improving the recognition results. This paper examines the mode of operation of the majority vote method in order to gain a deeper understanding of how and why it works, so that a more solid basis can be provided for its future applications to different data and/or domains. In the course of our research, we have analyzed this method from its foundations and obtained many new and original results regarding its behavior. Particular attention has been directed toward the changes in the correct and error rates when classifiers are added, and conditions are derived under which their addition/elimination would be valid for the specific objectives of the application. At the same time, our theoretical findings are compared against experimental results, and these results do reflect the trends predicted by the theoretical considerations.

Skeletonization algorithms have played an important role in the preprocessing phase of OCR systems. In this paper we report on the performance of 10 parallel thinning algorithms from this perspective by gathering statistics from their performance on large sets of data and examining the effects of the different thinning algorithms on an OCR system.>

Recently, it has been demonstrated that combining the decisions of several classifiers can lead to improved recognition results. The combination can be implemented using a variety of strategies, among which majority vote is by far the simplest, yet it has been found to be just as effective as more complicated schemes. However, all the results reported thus far on combinations of classifiers have been experimental in nature. The intention of this research is to analyze the foundations of the majority vote method in order to gain a deeper understanding and new results about its mode of operation.

The authors report on the performance of 10 parallel thinning algorithms from the perspective of character recognition. The algorithms evaluated include the complete range of four-subcycle, two-subcycle, and the most recent fully parallel methods. The authors consider different aspects of the performance of each algorithm. Statistics are gathered such as computing time, deviation from perfect 8-connectedness, and number of possible noise spurs present in the skeletons. In addition, the effects of each algorithm on an OCR system are examined through training and testing the system on the skeletons obtained from each thinning process.>

Whilst the design of skeletonization algorithms has been a very active research area, methodologies for an automatic evaluation of the quality of the results remain to be developed. The difficulty rests on the fact that certain geometric properties considered desirable in skeletons (especially for pattern recognition applications) are not easily quantifiable by means other than human vision. The attempt here is to develop distance measures based on shape matching methods, and to compare skeletons to references in terms of these distances. Three such methods have been adapted for this purpose, and the results are found to be highly correlated for the samples tested.

As a result of its central role in the preprocessing of image patterns, or because of its intrinsic appeal, the design of skeletonization algorithms has been a very active research area. However, few attempts have been made to evaluate the performance of different skeletonization algorithms. This paper presents the results of experiments to evaluate the performance of 20 skeletonization algorithms previously published in the literature. These algorithms have been implemented on the SUN 3/60 workstation in C and tested with a large variety of character patterns. A systematic comparison of these algorithms has been made based on the following criteria: reconstructibility, computation speed, similarity to the reference skeleton, quality of the skeleton, connectivity after skeletonization, and the degree of parallelism.

While the design of skeletonization algorithms has been a very active research area, methodologies for an automatic evaluation of the quality of the results remain to be developed. The attempt here is to develop distance measures based on shape matching methods, and to compare skeletons references in terms of these distances. Three such methods have been adapted for this purposes, and the results are found to be highly correlated for the samples tested.>

A comprehensive survey of thinning methodologies is presented. A wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, is covered. Skeletonization algorithms based on medial axis and other distance transforms are not considered. An overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern is given first. Thinning algorithms are then considered in terms of these criteria and their modes of operation. Nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels are discussed. The algorithms are considered in great detail and scope, and the relationships among them are explored.>

Four independently, developed expert algorithms for recognizing unconstrained handwritten numerals are presented. All have high recognition rates. Different experimental approaches for incorporating these recognition methods into a more powerful system are also presented. The resulting multiple-expert system proves that the consensus of these methods tends to compensate for individual weaknesses, while preserving individual strengths. It is shown that it is possible to reduce the substitution rate to a desired level while maintaining a fairly high recognition rate in the classification of totally unconstrained handwritten ZIP code numerals. If reliability is of the utmost importance, substitutions can be avoided completely (reliability=100%) while retaining a recognition rate above 90%. Results are compared with those for some of the most effective numeral recognition systems found in the literature.>