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When is a Problem Solved?
"... Abstract—Open problems are defined differently in document image analysis than in the physical sciences, theoretical computer science, or mathematics. Instead of a formal definition, problems in DIA are stated in terms of automation of an application area (e.g., postal address reading) or a scientif ..."
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Abstract—Open problems are defined differently in document image analysis than in the physical sciences, theoretical computer science, or mathematics. Instead of a formal definition, problems in DIA are stated in terms of automation of an application area (e.g., postal address reading) or a scientific subfield (e.g., image compression). The notion of a successful solution may be based on (1) the relative accuracy of automated vs. expert solutions (given specific data and degree of manual tuning); (2) the distinguishability of automated output from human output (a Turing Test); (3) the degree of current community interest (via conferences and journals); and/or (4) economic considerations. Because of the lack of formal definition for DIA problems, heuristics predominate over provably correct algorithms, and full disclosure of implementation details as well as populations and samples is essential. Results on available test sets are often only tangentially related to motivating applications. In addition, interest in automating certain tasks has been evolving rapidly as a result of advances in technology. Further community discussion of these issues may accelerate progress and symbiosis with allied disciplines. Keywords-document analysis; pattern recognition; performance evaluation; I.
Text Recognition in Street Level Images
"... Abstract — Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text. Segmentation of text in images is main step in OCR. Segmentation is the process of partitioning a ..."
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Abstract — Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text. Segmentation of text in images is main step in OCR. Segmentation is the process of partitioning a digital image into multiple segments (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Text segmentation in image is typically used to locate text region for further processing. In this paper, a novel method is proposed to location of text and normalized cross correlation based template matching to recognize characters in image.
Interactive, Mobile
"... Abstract. As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative approaches. Interactive classification is often more accurate then algorithmic classification, and requires less ti ..."
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Abstract. As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative approaches. Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. Recent technological advances like pocket computers, camera phones and wireless networks will have greater influence on mobile, distributed, interactive recognition of natural patterns than on conventional high-volume applications like mail sorting, check reading or forms processing. 1.
ABSTRACT Document Image Analysis for Digital Libraries
"... Digital Libraries have many forms – institutional libraries for information dissemination, document repositories for recordkeeping, and personal digital libraries for organizing personal thoughts, knowledge, and course of action. Digital image content (scanned or otherwise) is a substantial componen ..."
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Digital Libraries have many forms – institutional libraries for information dissemination, document repositories for recordkeeping, and personal digital libraries for organizing personal thoughts, knowledge, and course of action. Digital image content (scanned or otherwise) is a substantial component of all of these libraries. Processing and analyzing these images include tasks such as document layout understanding, character recognition, functional role labeling, image enhancement, indexing, organizing, restructuring, summarizing, cross linking, redaction, privacy management, and distribution. At the Palo Alto Research Center, we conduct research on several aspects of document analysis for Digital Libraries ranging from raw image transformations to linguistic analysis to interactive sensemaking tools. I shall describe a few recent research activities in the realm of document image analysis or their use in digital libraries.
Asymptotic cost in document conversion
"... In spite of a hundredfold decrease in the cost of relevant technologies, the role of document image processing systems is gradually declining due to the transition to an on-line world. Nevertheless, in some high-volume applications, document image processing software still saves millions of dollars ..."
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In spite of a hundredfold decrease in the cost of relevant technologies, the role of document image processing systems is gradually declining due to the transition to an on-line world. Nevertheless, in some high-volume applications, document image processing software still saves millions of dollars by accelerating workflow, and similarly large savings could be realized by more effective automation of the multitude of low-volume personal document conversions. While potential cost savings, based on estimates of costs and values, are a driving force for new developments, quantifying such savings is difficult. The most important trend is that the cost of computing resources for DIA is becoming insignificant compared to the associated labor costs. An econometric treatment of document processing complements traditional performance evaluation, which focuses on assessing the correctness of the results produced by document conversion software. Researchers should look beyond the error rate for advancing both production and personal document conversion.
