An image processing device includes multiple image processing units, each trained to accommodate a different feature possibly contained in an image, a decision unit that decides a sequence of the multiple image processing units according to the features contained in an input image, and an application unit that applies the image processing units to the input image in the sequence decided by the decision unit.

Methods and systems that detect and define virtual objects in remote screens which do not expose objects. This permits simple and reliable automation of existing applications. In certain aspects a method for detecting objects from an application program that are displayed on a computer screen is disclosed. An image displayed on the computer screen is captured. The image is analyzed to identify blobs in the image. The identified blobs are filtered to identify a set of actionable objects within the image. Optical character recognition is performed on the image to detect text fields in the image. Each actionable object is linked to a text field positioned closest to a left or top side of the actionable object. The system automatically detects the virtual objects and links each actionable object such as textboxes, buttons, checkboxes, etc. to the nearest label object.

A structured data processing system includes hardware processors and a memory in communication with the hardware processors. The memory stores a data structure and an execution environment. The data structure includes an electronic document. The execution environment includes a data extraction solver configured to perform operations including identifying a particular page of the electronic document; performing an optical character recognition (OCR) on the page to determine a plurality of alphanumeric text strings on the page; determining a type of the page; determining a layout of the page; determining at least one table on the page based at least in part on the determined type of the page and the determined layout of the page; and extracting a plurality of data from the determined table on the page. The execution environment also includes a user interface module that generates a user interface that renders graphical representations of the extracted data; and a transmission module that transmits data that represents the graphical representations.

There is disclosed a method of determining a digital document suitability for OCR processing, the method executable by a user electronic device, the user electronic device configured for capturing a digital image of a document. The method comprises: acquiring by the user electronic device, the digital image of the document; determining, by a classifier executed by the user electronic device, an OCR suitability parameter associated with the digital image, the OCR suitability parameter indicative of whether the digital image is suitable for producing an output of the OCR processing of an acceptable quality, the classifier having been trained to determine the OCR suitability parameter at least partially based on a level of noise associated with the digital image; in response to the OCR suitability parameter being below a pre-determined threshold, causing the user electronic device to re-acquire the digital image.

The subject matter of this specification can be implemented in, among other things, a method including identifying one or more blocks in an electronic image that depicts text characters. The method includes identifying one or more text blocks among the blocks that depict the text characters. The method includes identifying a text contrast value for each of the text blocks. The method includes identifying a type for each pixel in each of the text blocks based on the text contrast value. The method includes determining, for each pixel in each of the text blocks, a brightness for the pixel based on the identified type. The method includes storing, in at least one memory, the electronic image including the determined brightness for each pixel in each of the text blocks.

The subject matter of this specification can be implemented in, among other things, a method including identifying one or more blocks in an electronic image that depicts text characters. The method includes identifying one or more text blocks among the blocks that depict the text characters. The method includes identifying an average text contrast for each of the text blocks. The method includes identifying a type for each pixel in each of the text blocks based on the average text contrast. The method includes performing local adaptive filtering on a first neighborhood of pixels around each pixel in each of the text blocks to determine a brightness for the pixel based on the identified type. The method includes storing, in at least one memory, the electronic image including the determined brightness for each pixel in each of the text blocks.

A device for decoding magnetic patterns printed on documents comprising a reading head (12) having: a reader (20) arranged to read first magnetic signals belonging to the magnetic patterns and to electromagnetic noise due to sources internal and/or external to the device. The device further comprises: a further reader (40), arranged to read second magnetic signals belonging to the electromagnetic noise, an adder component (25) arranged to algebraically subtract the amplified second magnetic signals from the amplified first magnetic signals, and a converter (16) arranged to convert the resulting signal into a digital signal representing the read magnetic patterns. A method for decoding magnetic patterns is also disclosed.

A document management system uses an objective loss function to improve the performance of optical character recognition (OCR) processes on images of documents. A user captures an image of a document, which is imported into the document management system. The document management system performs OCR on a high resolution version of the image of the document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system compares the first and second sets of text in the form of an objective loss function. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.

A document management system uses an objective loss function to improve the performance of optical character recognition (OCR) processes on images of documents. A user captures an image of a document, which is imported into the document management system. The document management system performs OCR on a high resolution version of the image of the document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system compares the first and second sets of text in the form of an objective loss function. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.

Aspects and implementations provide for mechanisms of detection of fields in electronic documents and determination of values of the detected field. The disclosed techniques include obtaining an input into a machine learning model (MLM), the input including a first image of a field extracted from a document and depicting one or more static elements of the field and a field value, the input and further including a second image of the field. The input may be processed using the MLM to identify one or more static regions that correspond to static elements of the field. The identified static regions may be used to modify the first image in which the static regions are removed or have a reduced visibility. The modified image may be used to determine the field value.