Real-time evaluation of high quality preview images of a document prior to capturing a final image of the document on a mobile device is disclosed. In one aspect, an image capture device is activated on the mobile device. A plurality of preview images of the document is then captured, each of which has a lower image quality than a final image to be captured by the image capture device. Next, for each of the plurality of preview images, the system measures a parameter associated with the image capturing. Next, the system selects a preview image among a subset of the captured preview images when the value of the at least one parameter meets a threshold value.

Systems and methods are provided for processing and extracting content from an image of a driver's license captured using a mobile device. In one embodiment, an image of a driver's license (DL) is captured by a mobile device and corrected to improve the quality of the image. The corrected image is then further processed by cropping the image, identifying the format and layout of the DL, binarizing the image and extracting the content using optical character recognition (OCR). Multiple methods of image cropping may be implemented to accurately assess the borders of the DL, and a secondary layout identification process may be performed to ensure that the content being extracted is properly classified.

Real-time evaluation of high quality preview images of a document prior to capturing a final image of the document on a mobile device is disclosed. In one aspect, an image capture device is activated on the mobile device. A plurality of preview images of the document is then captured, each of which has a lower image quality than a final image to be captured by the image capture device. Next, for each of the plurality of preview images, the system measures a parameter associated with the image capturing, and related to the ability to accurately extract text and other content from a captured preview image of the document. Next, the system selects a preview image among a subset of the captured preview images that passes the image quality measurement. When a predetermined condition is met, the system chooses the selected preview image as a final captured image of the document.

The invention provides an English word image recognition method, mainly loading a to-be-recognized image and performing a one-dimensional convolutional neural network operation and a fully connected operation processing to generate a feature map, outputting the feature map by a bidirectional long short-term memory (LSTM) network and performing a fully connected operation to generate a feature map, then performing a probability recognition and outputting a probabilistic string, and then recognizing the probabilistic string and outputting a word recognition result to solve the problem of producing a large amount of operation in the conventional two-dimensional recognition operation, thereby achieving efficacies of reducing costs of recognition equipment and enabling fast and accurate recognition.

The invention provides an English word image recognition method, mainly loading a to-be-recognized image and performing a one-dimensional convolutional neural network operation and a fully connected operation processing to generate a feature map, outputting the feature map by a bidirectional long short-term memory (LSTM) network and performing a fully connected operation to generate a feature map, then performing a probability recognition and outputting a probabilistic string, and then recognizing the probabilistic string and outputting a word recognition result to solve the problem of producing a large amount of operation in the conventional two-dimensional recognition operation, thereby achieving efficacies of reducing costs of recognition equipment and enabling fast and accurate recognition.

A reading machine that operates in various modes includes image correction processing is described. The reading device pre-processes an image for optical character recognition by receiving the image and determining whether text in the image is too large or small for optical character recognition processing by determining that text height falls outside of a range in which optical character recognition software will recognize text in a digitized image. If necessary the image is resized according to whether the text is too large or too small.

A method, system and/or a digital camera provide a geometrical transformation of deformed images of documents comprising text, by text line tracking, resulting in an image comprising parallel text lines. The transformed image is provided as an input to an OCR program either running in a computer system or in a processing element comprised in said digital camera.

Described are techniques for font attribute detection. The techniques include receiving a document having different font attributes amongst a plurality of words respectively comprised of at least one character. The techniques further include generating a dense image document from the document by setting the plurality of words to a predefined size, removing blank spaces from the document, and altering an order of characters relative to the document. The techniques further include determining characteristics of the characters in the dense image document and aggregating the characteristics for at least one word. The techniques further include annotating the at least one word with a font attribute based on the aggregated characteristics.

A character recognition method for recognizing a character of a medium is provided. A character image of an individual character from a medium is acquired and the character image is read out step by step to determine the character according to a hierarchical structure in which a set of predetermined characters are hierarchically classified into a plurality of groups configured of main groups and sub groups.

The present disclosure relates to a font detection method using a neural network. The font detection method using the neural network according to the present disclosure includes receiving a target text image including a text; resizing a horizontal or vertical size to a reference input size according to an aspect ratio of the input target text image; and inputting the resized target text image into a trained neural network and outputting a font of the text included in the text image, and the neural network may be trained with a unit image extracted as a unit region of the reference input size from a training image generated by synthesizing a background with the text. According to the present disclosure, fonts according to various usage examples may be effectively detected.