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Extracting information from scanned invoices and other commercial documents, a critical component of corporate function, typically requires significant manual processing. Much research has been conducted in the field of automated information extraction and document processing to alleviate the manual resources used for document analysis, but resultant literature and commercially available products have demonstrated limitations in customizability for identifying specific information. In this paper, we propose a customized machine learning-based pipeline for extracting and tabulating relevant key–value pairs from commercial invoice documents. Specifically, the pipeline combines general document understanding, OCR extraction, and key–value matching with custom rules pertaining to a provided invoice dataset. Then, we demonstrate that the pipeline greatly outperforms a commercially available product and can significantly reduce the amount of manual labor required to process invoice documents. Future work will focus on generalizing the pipeline, so as to apply it on more varied datasets.

Extracting information from scanned business documents, while a necessary commercial task, continues to be mostly done manually, requiring significant human effort. Current solutions for automated document information extraction still have limited capabilities in regards to user-required customizability and extraction of dataset-specific information, leaving the area as a very active field of research. In this paper, we propose modifications and improvements to our previously developed custom pipeline for extracting and tabulating key-value pairs from commercial invoice documents. Our design changes and additions adapt the pipeline to a wider variety of document types and use cases, primarily through the implementation of dataset-specific configuration files that promote customizability along with new technical modules that address both general and dataset-specific complexities. We compare our pipeline’s performance against current machine learning and commercial solutions on a real-world dataset, and demonstrate that it is able to extract a wider variety of fields while maintaining competitive or greater accuracies compared to the alternate solutions.

A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system.

Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system.

Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.

A novel method is proposed in this study to recognize the line structure of table-form documents, e.g. telephone bills and office documents. The line structures of table-form documents are mainly composed of horizontal and vertical line segments. By treating the segment structure as line patterns, the problem of structure recognition is turned out to be the searching of line pattern matching, which can be solved by adopting the technique of relaxation. The proposed method consists of a learning phase and a recognition phase. In the former phase, line structures of various kinds of table-form documents are taken as templates and are extracted through a line extraction algorithm, in which an unique number functioning as a form ID is assigned to each line pattern. In the latter, by adopting the method of relaxation, the line pattern of the testing document is matched to those patterns created in the previous phase and the form ID of the best matching is chosen as the ID of the testing document. To increase the performance of the proposed method, an algorithm was presented to reduce the number of line segments in the matching process. The experimental results reveal the practicability of the proposed methods.

This paper presents the design of an intelligent document processing system, called TEXPROS. The system is a combination of filing and retrieval systems, which supports storing, extracting, classifying, categorizing, retrieving and browsing information from a variety of documents. TEXPROS is built based on object-oriented programming and rule-based specification techniques. In this paper, we describe main design goals of the system, its data model, logical file structure, and strategies for document classification and categorization. We also illustrate various retrieval methods and query processing techniques through examples. Finally applications of TEXPROS are presented, where we suggest ways in which the use of the system may alter the software process model.

Surveys of the basic concepts and underlying techniques are presented in this chapter. A basic model for document processing is described. In this model, document processing can be divided into two phases: document analysis and document understanding. A document has two structures: geometric (layout) structure and logical structure. Extraction of the geometric structure from a document refers to document analysis; mapping the geometric structure into logical structure deals with document understanding. Both types of document structures and the two areas of document processing are discussed in this chapter. Two categories of methods have been used in document analysis, namely, (1) hierarchical methods including top-down and bottom-up approaches, (2) no-hierarchical methods including modified fractal signatures. Tree transform, formatting knowledge and description language approaches have been used in document understanding. All the above approaches are presented in this chapter. A particular case — form document processing is discussed. Form description and form registration approaches are presented. A form processing system is also introduced. Finally, many techniques, such as skew detection, Hough transform, Gabor filters, projection, crossing counts, form definition language, etc. which have been used in these approaches are discussed in this chapter.

The basic concepts and underlying techniques of document processing are presented in this chapter. A basic model for document processing is described. In this model, document processing can be divided into two phases: document analysis and document understanding. A document has two structures: geometric (layout) structure and logical structure. Extraction of the geometric structure from a document refers to document analysis; mapping the geometric structure into the logical structure deals with document understanding. Both types of document structures and the two areas of document processing are discussed in this chapter. Two categories of methods have been used in document analysis, namely, (1) hierarchical methods including top-down and bottom-up approaches, (2) no-hierarchical methods including modified fractal signature and order stochastic filtering. Tree transform, formatting knowledge and description language approaches have been used in document understanding. All the above approaches are presented in this chapter. A particular case - form document processing is discussed. Form description and form registration approaches are presented. A form processing system is also introduced. Finally, many techniques, such as skew detection, Hough transform, Gabor filters, projection, crossing counts, form definition language, wavelet transform, etc. which have been used in these approaches are discussed in this chapter.

A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system.

Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system.

Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.

Extracting information from paper documents opens a variety of innovative applications by supporting people in their daily processing of documents. In this chapter, a system that interprets text on paper documents given the restricted domain of a certain application is presented. The system consists of four components. The Document Image Analysis component transforms the text of the scanned document image into an electronic format represented by a sequence of word hypotheses. Based on this sequence, three components extract the information necessary for automatic processing of documents. First, the information being enclosed in structured text is extracted, such as the sender and recipient of business letters, or title and author of scientific papers. Second, the text body of a message is mapped to a certain pre-defined category. In the final step, this text is analyzed and the information which is relevant for the current application is extracted. It is shown that for a real-world application the paper documents can be completely interpreted, resulting in an automatically generated answering letter. The system is fast, fault tolerant with respect to misspelling or recognition errors, and readily adaptable to new applications.

A basic model for document processing is presented in this chapter. In this model, document processing can be divided into two phases: document analysis and document understanding. A document has two structures: geometric (layout) structure and logical structure. Extraction of the geometric structure from a document refers to document analysis; mapping the geometric structure into logical structure deals with document understanding. Both types of document structures and the two areas of document processing are discussed in this chapter.

Top-down and bottom-up approaches have been used in document analysis. Tree transform, formatting knowledge and description language approaches have been used in document understanding. All the above approaches are presented.

A particular case—form document processing—is discussed. Form description and form registration approaches are presented. A form processing system is also introduced.

Finally, many techniques, such as Hough transform, projection, crossing counts, form definition language, etc. which have been used in these approaches are also discussed here.