Automatic document classification and indexing in high-volume applications

Abstract

In this paper a system for analysis and automatic indexing of imaged documents for high-volume applications is described. This system, named STRETCH (STorage and RETrieval by Content of imaged documents), is based on an Archiving and Retrieval Engine, which overcomes the bottleneck of document profiling bypassing some limitations of existing pre-defined indexing schemes. The engine exploits a structured document representation and can activate appropriate methods to characterise and automatically index heterogeneous documents with variable layout. The originality of STRETCH lies principally in the possibility for unskilled users to define the indexes relevant to the document domains of their interest by simply presenting visual examples and applying reliable automatic information extraction methods (document classification, flexible reading strategies) to index the documents automatically, thus creating archives as desired. STRETCH offers ease of use and application programming and the ability to dynamically adapt to new types of documents. The system has been tested in two applications in particular, one concerning passive invoices and the other bank documents. In these applications, several classes of documents are involved. The indexing strategy first automatically classifies the document, thus avoiding pre-sorting, then locates and reads the information pertaining to the specific document class. Experimental results are encouraging overall; in particular, document classification results fulfill the requirements of high-volume applications . Integration into production lines is under execution.

Text/Graphics Separation Revisited

“…However, whereas RLSA filtering has proved its efficiency in segmenting textual documents, its use in graphics-rich documents is less frequent; one of the few methods we are aware of is that of Lu [13]. Other methods used for text-rich documents include those based on white streams [18] and the top-down methods using some kind of X-Y decomposition of the document [1,16].…”

Tsallis Mutual Information for Document Classification

“…The first one concerns data-based systems and the second one concerns model-based systems. Data-based systems are usually used in heterogeneous document flows and extract different information from documents, such as tables [26], graphical features such as logos and trademarks [27], or the general layout [23]. On the contrary, model-based systems are used in homogeneous document flows, where similar documents arrive generally one after the other [28][29][30][31].…”

“…The documents of the same class can share some interesting information such as the background color, the document layout, the position of the relevant information on the image, or metadata, such as the seller. Once one document is grouped into a class, an specific processing for extracting the desired information can be designed depending on these features [23]. A simple way to define a class consists in taking a representative image.…”

“…In the literature of document image classification, different measures of similarity have been used. Appiani et al [23] design a criterion to compare the structural similarity between trees that represent the structure of a document. Shin and Doermann [24] use a similarity measure that considers spatial and layout structure.…”

“…However, many applications require to deal with a great variety of layouts, where relevant information is located in different positions. In this case, it is necessary to recognize the document layout and apply the appropriate reading strategy [23]. Several strategies have been proposed to achieve an accurate document classification based on the layout analysis and classification [1,4,5,[23][24][25].…”