Systems, methods and non-transitory computer readable media for initiating actions based on an ongoing customer journey are provided. Customer journey data associated with an ongoing customer journey may be received. The ongoing customer journey may involve an individual and a device associated with the individual in a retail store, such as a shopping cart. The customer journey data may indicate a trajectory of the device in the retail store generated based on data captured using an indoor positioning instrument associated with the device. While the ongoing customer journey is in progress, the customer journey data may be analyzed to determine information associated with the individual. The information associated with the individual may be used to select an action associated with the individual. A digital signal configured to initiate the selected action may be generated.

The present application relates to layout analysis on an image. The layout analysis method comprises: obtaining coordinate information of one or more character lines in an image; generating a layout model corresponding to the image by setting character data in regions in a data structure that correspond to the coordinate information of the one or more character lines, the character data including data indicating the presence of character; scanning the generated layout model to read the character data from the layout model, and performing paragraph division on the layout model based on a relative positional relationship of the read character data in the layout model.

Implementations for optical character recognition of a language script can include actions of receiving an image comprising a graphical representation of a word written in the language script, segmenting the word into two or more segments, each segment being determined based on one or more of a variation in a height of the word and a variation in a width of the word, and including at least one character, providing a boundary for each segment of the two or more segments, the boundary enclosing the at least one character of a respective segment, each boundary having an edge with respect to an axis of the image, normalizing boundaries of the two or more segments by aligning edges of the boundaries, and labeling each segment of the two or more segments with a respective label, the respective label indicating a language character within the respective boundary.

Systems and methods for merging adjacent word fragments in outputs of optical character recognition (OCR) systems can include a processor obtaining word fragments associated with OCR data generated from an image. Each word fragment can be associated with a respective text line of a plurality of text lines. The at least one processor can determine, for each pair of adjacent word fragments in a text line, a respective normalized horizontal distance between the pair of adjacent word fragments. The processor can identify one or more pairs of adjacent word fragments that are candidates for merging based on the determined normalized horizontal distances. The processor can determine that a pair of adjacent word fragments, among the one or more pairs of adjacent word fragments that are candidates for merging, matches a predefined expression of a plurality of predefined expressions, and merge that pair of adjacent word fragments into a single word.

Provided is a character segmentation and recognition method. The method includes: collecting image data to obtain a to-be-recognized image; positioning a character line candidate region on the to-be-recognized image; obtaining pre-set character line prior information, where the character line prior information includes the number of characters, character spacing and a character size; obtaining a corresponding segmentation point template based on the character line prior information; obtaining credible degrees of different positions on the character line candidate region traversed by the segmentation point template; determining a position with the highest credible degree as an optimal segmentation position; segmenting the character line candidate region based on the segmentation point template and the optimal segmentation position to obtain multiple single character regions; and performing character recognition on each of the single character regions to obtain a corresponding recognition result.

In an intelligent character recognition (ICR) method for recognizing hand-written text images using a long-short term memory (LSTM) recurrent neural network (RNN), text images are segmented into text line images, and the text lines images are pre-processed to normalize the line height and to equalize the word spacings in each text line. Both training images used to train the RNN network and test images containing text to be recognized by the trained RNN network are pre-processed to have identical heights and identical word spacings between words. This method improves character recognition accuracy.

In an intelligent character recognition (ICR) method for recognizing hand-written text images using a long-short term memory (LSTM) recurrent neural network (RNN), text images are segmented into text line images, and the text lines images are pre-processed to normalize the line height and to equalize the word spacings in each text line. Both training images used to train the RNN network and test images containing text to be recognized by the trained RNN network are pre-processed to have identical heights and identical word spacings between words. This method improves character recognition accuracy.

Speed and accuracy of character recognition can be improved by isolating text orientation during an early stage of processing an image containing a mixture of horizontal and vertical text. Vertical and horizontal line bounding boxes are defined from characters in the image. In a section of the image containing horizontal text, vertical line bounding boxes may tend to be larger and/or spaced close together due to misalignment of characters. For the same reason, horizontal line bounding boxes may tend to be larger and/or spaced closed together in a section of the image containing vertical text. Such variations in size and/or spacing may be used to identify a division between the horizontal and vertical text. A subsequent character recognition process may take advantage of a known division to conserve computing resources.

Speed and accuracy of character recognition can be improved by isolating text orientation during an early stage of processing an image containing a mixture of horizontal and vertical text. Vertical and horizontal line bounding boxes are defined from characters in the image. In a section of the image containing horizontal text, vertical line bounding boxes may tend to be larger and/or spaced close together due to misalignment of characters. For the same reason, horizontal line bounding boxes may tend to be larger and/or spaced closed together in a section of the image containing vertical text. Such variations in size and/or spacing may be used to identify a division between the horizontal and vertical text. A subsequent character recognition process may take advantage of a known division to conserve computing resources.

An electronic device for extracting a distance of an object and displaying information and a method thereof. The method includes extracting relative distance information of at least two or more objects using a pixel distribution diagram sensed using a sensor of the electronic device and displaying information on an object with a relative value in which the extracted relative distance information and object analysis information are reflected.