Image processing apparatus and image processing method for managing settings to allow or prohibit a character recognition function

    公开(公告)号:US11861253B2

    公开(公告)日:2024-01-02

    申请号:US18114223

    申请日:2023-02-25

    发明人: Tatsuya Fujisaki

    IPC分类号: G06F3/12 G06V30/10

    摘要: According to an embodiment, an image processing apparatus includes: a character recognition processor which reads an image of a document and extracts text information in the document; a setting manager which manages settings including a setting to allow or prohibit a function of character recognition; a job controller which controls job execution related to reading of the document; and an operation controller which provides a setting menu to receive a setting of at least one item related to the job execution and receives a setting, in which when the function of the character recognition is set to be prohibited, the operation controller hides a function that requires the character recognition from the setting menu or indicates that the function is not to be set, and when the function that requires the character recognition has already been set, the operation controller enables the function to be replaced by another function.

    SEGMENTATION OF PAGE STREAM DOCUMENTS FOR BIDIRECTIONAL ENCODER REPRESENTATIONAL TRANSFORMERS

    公开(公告)号:US20230410541A1

    公开(公告)日:2023-12-21

    申请号:US17843991

    申请日:2022-06-18

    发明人: Yury Ageev

    摘要: Systems and methods relate generally to performing a machine learning task on training documents to generate an output. In an example method, a pretrained Sentence Bidirectional Encoder Representational Transformers (“S-BERT”) model is obtained. The training documents are scanned by a plurality of scanners. Content of the training documents is recognized with character recognition. The content is templated responsive to the character recognition. The content is processed with the pretrained S-BERT model for training thereof. A trained S-BERT model is generated from the processing of the content as the output. The trained S-BERT model is configured to automatically categorize and assemble non-training documents into original configurations thereof.