A system, comprising: at least one processor; and memory including instructions that, when executed by the at least one processor, cause the system to: receive a digitized object, the digitized object corresponding to an image including at least one symbol corresponding to a letter, number, character, or a combination thereof; form digitized line blocks from the digitized object by determining whitespace separators in the digitized object; convert the line blocks into one or more word blocks separated by whitespace; resize each word block of the one or more word blocks into a canonical representation of a fixed height; evaluate each word block of the one or more word blocks using a neural network, the neural network including a dictionary, the dictionary containing a collection of common words; output a first word prediction when one or more of the word blocks corresponds to a word in the collection; and when the one or more word blocks do not correspond to the collection: determine a number of symbols and a number of blanks in each word block of the one or more word blocks using a fully convolutional neural network; evaluate the symbols in the one or more word blocks, the evaluation identifying a value associated with the symbols in the one or more word blocks; perform probabilistic correction on the one or more word blocks; and output a second word prediction, the second word prediction corresponding to the value of the symbols forming the word block. at least one processor; and memory including instructions that, when executed by the at least one processor, cause the system to: receive a digitized object, the digitized object corresponding to an image including at least one symbol corresponding to a letter, number, character, or a combination thereof; form digitized line blocks from the digitized object by determining whitespace separators in the digitized object; convert the line blocks into one or more word blocks separated by whitespace; resize each word block of the one or more word blocks into a canonical representation of a fixed height; evaluate each word block of the one or more word blocks using a neural network, the neural network including a dictionary, the dictionary containing a collection of common words; output a first word prediction when one or more of the word blocks corresponds to a word in the collection; and when the one or more word blocks do not correspond to the collection: determine a number of symbols and a number of blanks in each word block of the one or more word blocks using a fully convolutional neural network; evaluate the symbols in the one or more word blocks, the evaluation identifying a value associated with the symbols in the one or more word blocks; perform probabilistic correction on the one or more word blocks; and output a second word prediction, the second word prediction corresponding to the value of the symbols forming the word block. receive a digitized object, the digitized object corresponding to an image including at least one symbol corresponding to a letter, number, character, or a combination thereof; form digitized line blocks from the digitized object by determining whitespace separators in the digitized object; convert the line blocks into one or more word blocks separated by whitespace; resize each word block of the one or more word blocks into a canonical representation of a fixed height; evaluate each word block of the one or more word blocks using a neural network, the neural network including a dictionary, the dictionary containing a collection of common words; output a first word prediction when one or more of the word blocks corresponds to a word in the collection; and when the one or more word blocks do not correspond to the collection: determine a number of symbols and a number of blanks in each word block of the one or more word blocks using a fully convolutional neural network; evaluate the symbols in the one or more word blocks, the evaluation identifying a value associated with the symbols in the one or more word blocks; perform probabilistic correction on the one or more word blocks; and output a second word prediction, the second word prediction corresponding to the value of the symbols forming the word block. determine a number of symbols and a number of blanks in each word block of the one or more word blocks using a fully convolutional neural network; evaluate the symbols in the one or more word blocks, the evaluation identifying a value associated with the symbols in the one or more word blocks; perform probabilistic correction on the one or more word blocks; and output a second word prediction, the second word prediction corresponding to the value of the symbols forming the word block.