An example computing system is configured to: (i) access a drawing associated with a construction project; (ii) identify, in the drawing, a set of candidate textual elements that potentially represent a title of the drawing; (iii) for each candidate textual element, (a) determine a respective dataset comprising values for a set of data variables that are potentially predictive of whether the candidate textual element is the title of the drawing, and (b) input the respective dataset into a machine-learning model that functions to (1) evaluate the respective dataset and (2) output, based on the evaluation, a respective score indicating a likelihood that the candidate textual element represents the title of the drawing; and (iv) based on the respective scores for the candidate textual elements that are output by the machine-learning model, select one given candidate textual element as the title of the drawing.

An electronic pen is communicable with a tablet and includes a communication circuit configured to receive haptics data that is associated with stroke data and transmitted from the tablet. The electronic pen includes a vibration circuit configured to generate tactile feedback for a user. The electronic pen also includes a controller circuit configured to control the vibration circuit to generate the tactile feedback based on the haptics data received by the communication circuit.

An image forming apparatus capable of controlling execution of inspection without increasing a time period required to complete printing. On a registration screen, whether or not to execute inspection of data to be printed is set, and keywords indicative of confidentiality are registered. Text information is extracted from the data to be printed, and whether or not any keyword matching the text information has been registered is determined. Execution of print processing of the data to be printed is controlled based on a result of the determination. When non-execution of inspection is set, the print processing of the data to be printed is executed without executing the determination, whereas when execution of inspection is set, the print processing of the data to be printed is controlled based on a result of the determination.

An image processing apparatus includes at least one memory that stores instructions; and at least one processor that execute the instructions to perform: detecting text blocks in an input image; determining a registered document corresponding to the input image among a plurality of registered documents; determining the text block in the input image that corresponds to a processing target item, based on a partial layout defined in the determined registered document and including a first text block corresponding to the processing target item and at least one second text block present near the first text block; and obtaining a character string corresponding to the processing target item by performing character recognition processing on the determined text block.

The present invention is directed to a computer application for analyzing handwriting. The handwriting is digitized by being captured by a computing device such as a tablet. The application analyzes four components of the digitized handwriting. The initial component provides real-time writing speed feedback to the subject. The second fully automated component computes a variety of kinematic measures based on periods of time when the subject is writing versus the pen being off the tablet. A third component is able to concatenate pen strokes into user defined characters and assesses character and/or word spacing based on preset distances. For the fourth component, a 2-dimensional version of the large deformation diffeomorphic metric mapping (LDDMM) method is used to compare each character to a template character. Together, these components can be used to assess handwriting for a broad range of applications.

Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data for a vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for the vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. A determination may be made as to whether the object is a static object. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.

A vision-based product inspection system captures multiple images of each of multiple individual instances of a product as each instance passes through various phases of a production process. The system includes multiple cameras with each camera situated at a known location along a moving conveyor, conveyor belt, production line, or assembly line that moves instances of the product through various phases of the production process. Each camera can be associated with a known location along the conveyor and each image can be associated with a value representing the position of the conveyor as it moves product. Based on each camera's location and the values representing the conveyor's position, a sequence of images can be accumulated representing the progression of any single instance of a product as it moves through the production process. Automated quality control inspection can be performed by comparing or analyzing images in the sequence.

Retailers need information about their competitor's pricing and promotions and marketing collaterals are one of the most vital sources of this information. Conventional approaches for extracting product names from marketing collaterals depends on large volume of data repositories and complex machine learning based approaches. The present disclosure extracts product name blocks from marketing collaterals using image processing techniques. The inputs to the present disclosure are seed words and the marketing collateral. A plurality of word level text regions from the image and text value are obtained. Further, a plurality of text characteristics corresponding to each of the plurality of word level text regions are extracted and matching seed word regions are obtained. Further a plurality of meaningful text blocks and a plurality of seed blocks are obtained. Finally, a plurality of product names are extracted using a matrix based product name detection technique and updated in the product dictionary.

Aspects and implementations provide for techniques of fast and efficient recognition of texts in electronic documents. The disclosed techniques include, for example, accessing a description of a symbol in a page description file for a document and identifying, responsive to a character code failure, the symbol using a vectorized drawing instruction for the symbol. The character code failure includes an absence of a character code in the description of the symbol or a bad character code in the symbol description of the symbol. The techniques further include identifying a text of the document using the identified symbol.

A method may include receiving a primary document including one or more selection boxes, one or more text lines, and one or more annotations. The method may include determining, a class based on the annotations. The method may include identifying the one or more selection boxes and one or more text lines of the primary document. The method may include generating a graph representing the one or more selection boxes and the one or more text lines. The method may include mapping each of the one or more selection boxes to a respective text line of the one or more text lines of the graph based at least in part on one or more characteristics associated with the selection boxes. The method may include generating a key-value pair associated with each of the one or more text lines and generating a document model of the primary document.