Binarization is a well-known image processing task, whose objective to separate the foreground of an from background. One many tasks for which it useful that preprocessing document images in order identify relevant information, such as text or symbols. The wide variety types, alphabets, and formats makes binarization challenging. There are multiple proposals with solve this problem, classical m...

Smartphones have enabled effortless capturing and sharing of documents in digital form. The documents, however, often undergo various types degradation due to aging, stains, or shortcoming environment such as shadow, non-uniform lighting, etc., which reduces the comprehensibility document images. In this work, we consider problem image cleanup on embedded applications smartphone apps, usually m...

Searching in a large heterogeneous collection of scanned document images often produces uncertain results in part because of the size of the collection and the lack of an ability to focus queries appropriately. Searching for documents by their type is a natural way to enhance the effectiveness of document retrieval in the workplace [2], and a such system is proposed in [4]. The goal of our work...

In this chapter, we discuss the use of Self Organizing Maps (SOM) to deal with various tasks in Document Image Analysis. The SOM is a particular type of artificial neural network that computes, during the learning, an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning in document image ana...

Field trials of the LTS-3 system at Keesler Air Force Base have been extended, and excellent results have been obtained with high-aptitude students, who had been excluded from earlier trials. A study of the use of the LTS for task simulation has led to the implementation of a new student response interpretation feature for the system. Design of the microfiche selector/reader breadboard for LTS-...