1. Field of the Invention
The embodiments of the invention provide an apparatus and method for a camera-equipped writing tablet for digitizing form entries.
2. Description of the Related Art
Computerized physician order entry systems (CPOE) reduce clinical errors and streamline the order entry process in hospitals [1]. The current practice of converting hand-filled forms to electronic records is slow, taking usually 2-3 weeks for the filled form entries to be available to physicians in their clinical information systems. Such delays in transcoding as well as the human data entry errors are the leading cause of clinical errors. Automatic data entry using off-line form recognition is usually not an option in such cases due to the handwritten entries on the forms. In spite of this, fewer that 6% of the hospitals in the United States have adopted complete CPOE systems. The two main reasons for their lack of adoption are the cost and physicians' acceptance of the devices, as now they are responsible for the data entry themselves through order entry devices. CPOE systems for large hospitals are a fairly expensive (>$1 Million) proposition, requiring investment in order entry devices such as tablet PCs or wireless PDAs and their associated backend servers and medical data warehouses. While cost is a consideration, the lack of physician's adoption of such devices is a problem of greater concern as it appears to be due to lack of suitable devices that provide the expected ease-of-use. While PDAs are cost effective, their small form factor (approximately A8 size page) makes entering data difficult. Presenting all the information needed in an aesthetically viewable format is also difficult, in general, due to the size of the associated displays. Forms are often displayed in portions only leaving an incomplete overall picture of the choices made by the physicians. Images, in particular, cannot be displayed at a resolution needed for diagnostic decision making. Augmenting PDAs with better projection displays would increase the cost and make them less ubiquitous. In contrast, the Tablet PCs has sufficient display size for a good viewing experience. However, they are expensive, and being full-scale computers, are an ‘overkill’ for order entry purposes. Further, they are heavy (˜1.5 kg) and fragile (e.g. glass display, disk drive), may need frequent recharging, and are usually targets of theft.
Thus, price and ease-of-use are two major barriers to the adoption of CPOE systems in hospitals. In surveys of physicians, a popular question is, “How can physicians be best encouraged to use CPOE systems?” The surprising answer is that for most order-entry purposes, they actually prefer the ease-of-use offered by paper forms. It is still easier and faster to tick off boxes on a form, scrawl an order in a chart, than to log on to a computer system, call up the appropriate screen, examine the information presented, and click or type a response. Traditional paper forms create a lower cognitive load, allowing the medical practitioner to focus their attention on the patient, rather than the tool. It is also simple, inexpensive, light-weight and easy to maintain.
Thus, to reduce clinical errors as well as increase physicians' acceptance, it appears that an order-entry device is needed that preserves the familiar experience of filling out a paper form while allowing automatic conversion of filled content into electronic records. Automatic form and field identification are challenging problems. Ease of use considerations prefer that the camera be placed in unobtrusive locations leading to significant perspective distortion in the captured images. This makes reliable identification of forms difficult. Also, since the camera is very close to the imaged object (form), weak perspective projection models do not hold, requiring the use of projective transforms. Since pose registration errors can lead to the wrong field labels being assigned to the tablet, careful analysis of the projection geometry of the camera, the tablet, and the original form is performed.
Camera-assisted form tablets have not been introduced in research literature or commercially on the market. The technology exploited herein is based on prior work on object indexing and form recognition. In particular, recognition of scanned forms has been addressed by a number of researchers [6, 7, 11, 12, 13-17]. Several low-level form processing and feature extraction methods [11, 12] exist including those that analyze layout [7, 18], fields [15], and hand-filled entries [12, 13]. Registration methods based on projective geometry have been used for scanned form alignment and recognition [16]. While almost all form recognition work assumes printed forms, a document imaging camera system by XEROX (Stamford, Conn., U.S.A.) is available. The focus in this system has been on image processing of the document to filter, de-skew and produce better document appearance rather than form identification and automatic field extraction. The predominant techniques for identifying the form type use bar codes or OMR technology. The recognition of printed text on forms is done fairly well using commercial OCR engines and most OCR software also offer their engine bundled in form recognition software. The recognition of handwritten text, however, is still an open problem for scanned forms. Thus the problem of identification of forms from camera-grabbed images as well as the indexing of such form databases has not been addressed.
The work on form indexing herein is based on the technique of geometric hashing previously introduced for the model indexing problem in computer vision [2]. Several variants of this technique have appeared in literature including line hashing [3] where the basis space was formed from lines, location hashing [4] and region hashing. The sensitivity analysis of geometric hashing was done in [5] where the limitations of the technique were described. While hashing has been attempted with line basis or point basis, the combination of point basis space and line affine-invariants has not been explored.
USPTO Backgrounds
USPTO Backgrounds