Optical character recognition employing deep learning with machine generated training data

A computerized optical character recognition system comprising:

• a computerized deep learning system trained to process a plurality of encoded images within a particular domain to identify images representing text within each encoded image and converting the encoded images representing text to textually encoded data;
• data storage for storing the encoded images representing text and textually encoded data;
• wherein the computerized deep learning system is trained with training data generated from a corpus of,
• real-life text segments generated by processing from a corpus of encoded document images, at least a subset of encoded images from the corpus, with a plurality of OCR modules, each of the OCR modules processing each encoded image from the corpus to produce a real-life image/text tuple, and at least some of the OCR modules producing a confidence value corresponding to each real-life image/text tuple, and wherein each OCR module is characterized by an conversion accuracy substantially below a desired accuracy for an identified domain;
• and
• synthetically generated text segments, generated by programmatically converting each of a plurality of text strings to a corresponding encoded image, each text string and corresponding encoded image forming a synthetic image/text tuple.

A computerized system for training a computerized deep learning system utilized by an optical character recognition system comprising:

• a processor configured to execute instructions that when executed cause the processor to:
• generate a plurality of synthetic text segments, by programmatically converting each of a plurality of text strings to a corresponding image, each text string and corresponding image forming a synthetic image/text tuple;
• and
• generate a plurality of real-life text segments by processing from a corpus of document images, at least a subset of images from the corpus, with a plurality of OCR modules, each of the OCR modules processing each image from the subset to produce a real-life image/text tuple, and at least some of the OCR modules producing a confidence value corresponding to each real-life image/text tuple, and wherein each OCR module is characterized by an conversion accuracy substantially below a desired accuracy for an identified domain;
• and
• data storage, operatively coupled to the processor, for storing the synthetic image/text tuple and the real-life image/text tuple as training data in a format accessible by the deep learning system for training, wherein the computerized system employs the training data to train the deep learning system.