So the model should not immediately classify the segmented characters but should find the "nearest" characters in every class that optimises the combined probability of characters of the word being one of the classes of words we have in our dictionary. Fate intervened in the form of a neighbour collecting jumble who just so happened to be a qualified knitter and had a whole slew of like-minded friends. In the second picture we recognised the words even if they are not made of english characters because our brain doesn't scan the text character by character like "7-H " but treats it like an image. This word descrambler can help you unscramble a letter jumble to find words. Figure out what the word is and write it on the blank line provided. We generally index words based on the first syllable/character in our brain, like or even this The letters of the words below are jumbled. Įven when humans solve jumble we do it in a systematic/algorithmic way, if the jumbled word is not present in our memory we can't do anything otherwise we can solve the jumble.īut human brain can simply recognize scrabled words if some of the structure is retained and not fully scrambled like an anagram. What is an anagram An anagram is formed when letters in a name, word or phrase are rearranged into another name, word or phrase. you could implement an algorithm that converts each word into a set of its characters, then pick the word in your master list with minimum distance based on its character list. This is given as a problem in facebook engineering puzzles.īut i doubt that it is not much of a machine learning/AI problem.
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