Machine Learning in

Document Analysis and Recognition


by Simone Marinai and Hiromichi Fujisawa (Eds.)

Springer, 2008


Reviewed by:  L. Venkata  Subramaniam (India)


This book is a collection of research papers and reviews linking together document analysis and recognition (DAR) research with machine learning research. Stated goals of the book’s editors are: the identification of good practices for the use of learning strategies in DAR, identification of DAR tasks more appropriate for learning strategies, and highlighting new learning algorithms that may be successfully applied to DAR. The papers in this book cover different topics in DAR including layout analysis, text recognition, and classification.

Document analysis and recognition is a mature field of research. The first papers in this area appeared in the 1960’s. This book has sixteen papers covering pretty much the most recent research in this area. The editors mention that they have deliberately not grouped the papers so that readers can choose their own path through the book. However, the first paper gives an introduction to DAR and ties the whole book together by citing the papers in the book under appropriate sections. This is the must read chapter of the book.

Several papers cover physical layout analysis, with one covering logical layout analysis. Text recognition is a widely studied topic that has resulted in many applications and products. Still there are challenges in dealing with noisy documents and non-standard fonts. There are several papers covering both online and offline recognition of characters and words. Supervised and unsupervised classifiers have been considered for various tasks like pixel and region classification, reading order detection, text recognition, character segmentation, script identification, signature verification, writer identification, and document categorization.

Neural networks, inductive logic programming, support vector machines, latent semantic indexing, and a host of other machine learning techniques have been applied to the various DAR tasks in this book. Indeed this book is about learning methods that can be used in DAR. Each of the papers has an experiments section where the proposed approaches have been evaluated on actual datasets including several public ones.

The collection of papers in this book will prove useful for an advanced researcher in the field or graduate students planning to do a thesis in DAR. The book would also be very useful for researchers in machine learning to understand key applications of learning approaches.

Click above to go to the publisher’s web page where there is a description of the book, a link to the Table of Contents, and sample pages.

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