An information processing apparatus includes circuitry that: receives correction content indicating a change from a first character string extracted from first document data to a second character string; stores in a memory a document correction history representing the correction content of the first document data, and identical document information used for determining whether an input document has an identical format with the first document data; acquires second document data as the input document; extracts a third character string from the second document data; calculates a degree of match between the second document data and the identical document information; and when the degree of match is equal to or greater than a threshold value, and a comparison result between the third character string and the document correction history meets a predetermined condition, corrects the third character string based on the correction content represented by the document correction history.

Image processing techniques are described, including techniques in which text data associated with an image is used to determine a font of text in an image. The image is split into a plurality of crops based on the text data. A trained machine learning model is used to determine feature vectors of the image. The feature vectors are combined into a combined feature vector. A second trained machine learning model is used to determine a font using the combined feature vector. The second trained machine learning model may be a multi-layer perceptron network. The second trained machined learning model may be trained on a plurality of images with text of known fonts and properties. The described image processing techniques also include text removal.

At least one processor of an image processing system acquires a target object image including a target object. The at least one processor executes matching for the target object image based on a template image including a feature about the target object. The at least one processor analyzes a plurality of scores acquired through the matching. The at least one processor determines, based on a result of analysis of the plurality of scores, whether the target object in the target object image is blurred.

Techniques for recognizing characters in an image of a physical artifact may involve identifying contours in the image and sorting identified contours into different groups. A first group of contours may be analyzed to locate an array of lines in the image of the bank check. Locating each line in the line array may involve processing image data to allocate each contour in the first group to a particular line. After the array of lines is located, a second group of contours that each intersects a line may be analyzed. Any portions of each of the second group of contours that fit within upper and lower boundaries of a given line are added to the given line. After processing the first and second groups of contours, contours within a given line are analyzed to determine any individual identifiable characters. Each character may then be analyzed for character recognition.