Identification of reading order text segments with a probabilistic language model

A computing system comprising:

• a processing unit;
• and
• a non-transitory computer-readable medium communicatively coupled to the processing unit, wherein the processing unit is configured for executing program code stored on the non-transitory computer-readable medium and thereby performing operations comprising:
• accessing a probabilistic language model of observed word sequence patterns that computes (i) a first likelihood that a phrase obtained by splicing a text segment together with a continuation text segment has a correct reading order and (ii) a second likelihood that an additional phrase obtained by splicing the text segment together with another continuation text segment has the correct reading order, wherein the probabilistic language model is (i) trained with tokenized data obtained from a first text corpus for a given language and (ii) validated against a second text corpus distinct from the first text corpus, the tokenized data comprising tokens respectively corresponding to sets of textually encoded characters generated from the first text corpus,
• providing, to the probabilistic language model, (i) a first set of text segments having a first text segment and a corresponding first continuation text segment and (ii) a second set of text segments having the first text segment and a corresponding second continuation text segment,
• computing, with the probabilistic language model, a first score and a second score, the first score indicating a likelihood of the first text segment and the corresponding first continuation text segment providing a correct structured reading-order sequence, the second score indicating a likelihood of the first text segment and the corresponding second continuation text segment providing the correct structured reading-order sequence, and
• outputting, based on the first score and the second score, a reading order text extraction for sequencing a set of text strings on a display device.

A non-transitory computer-readable medium having executable program code stored thereon that, when executed by a processing unit, configures the processing unit to perform operations comprising:

• accessing a probabilistic language model of observed word sequence patterns that computes (i) a first likelihood that a phrase obtained by splicing a text segment together with a continuation text segment has a correct reading order and (ii) a second likelihood that an additional phrase obtained by splicing the text segment together with another continuation text segment has the correct reading order, wherein the probabilistic language model is (i) trained with tokenized data obtained from a first text corpus for a given language and (ii) validated against a second text corpus distinct from the first text corpus, the tokenized data comprising tokens respectively corresponding to sets of textually encoded characters generated from the first text corpus;
• providing, to the probabilistic language model, (i) a first set of text segments having a first text segment and a corresponding first continuation text segment and (ii) a second set of text segments having the first text segment and a corresponding second continuation text segment;
• computing, with the probabilistic language model, a first score and a second score, the first score indicating a likelihood of the first text segment and the corresponding first continuation text segment providing a correct structured reading-order sequence, the second score indicating a likelihood of the first text segment and the corresponding second continuation text segment providing the correct structured reading-order sequence;
• and
• outputting, based on the first score and the second score, a reading order text extraction for sequencing a set of text strings on a display device.