An improved graphology system

An improved graphology system, wherein said system is time-saving and occupies less memory space, detects different handwriting traits based on the user's handwriting and comprises of:

• Means of Processing (P);
• Means of Analyzing Handwriting Traits (A);
• Means of Finalizing Output (F);
• Wherein,
• Said Means of Processing (P) further comprises of:
• An Input Means (1);
• Means of Image Inversion (2);
• Means of Generating Sub-images (3);
• Means of OCR Model (4);
• Wherein;
• Said Input Means (1) configured to scan or capture a handwritten file or scanned copy as an image through digital devices, which is read and forwarded to said Means of Image inversion (2) for performing an image inversion process, where these images are converted from a dark area to a light area and vice versa, said images are then sent to said Means of Generating Sub-images (3) which produces four equal sub images for extracting the lines from text images for different forms of trait extraction, these sub-images are sent to said Means of OCR (Optical Character Recognition) Model (4) configured to process and extract these sub images into an accessible electronic text version which are then sent through said Means of Analyzing Handwriting Traits (A).
• Wherein,
• Said Means of Analyzing Handwriting Traits (A) configured to process all the traits of handwriting analysis that varies within an individual's handwriting. These traits include:
• The baseline calculation which is examined from the words extracted through said Means of OCR (Optical Character Recognition) Model (4). These words are then forwarded to Means of Line Extraction and Baseline Calculation (5) which examines all the words of the OCR and identifies each line of the words, thereby determining all the lines for calculating the baseline trait and generates Output 1.
• The slant angle calculation of individual characters which is examined from the words extracted through said Means of OCR (Optical Character Recognition) Model (4). These words are forwarded for character extraction from individual words through Means of Characters extraction (6) and these extracted characters are calculated to find their lowest starting point, lowest ending point and highest upstroke point and from these extracted character points, an angle is calculated through the Means of Slant calculation (8) and the said angle is selected from the slant category which is further analyzed and compiled as Output-2.
• The zone calculation of individual words which is examined from the words extracted through said Means of OCR (Optical Character Recognition) Model (4). These extracted words are then forwarded to Means of Word Pre-processing (9) for the removal of any extraneous white portions from the word images, which is carried out by the inversion process and these words are cleaned by cancelling the noise through Means of Clean Word Images (10) and sent to Means of Extraction and Cropping characters (11) for extracting each character from clean word and these extracted characters are then forwarded to Means of Character Pre-processing (12) for removal of any extra white portions from the characters and these characters are cleaned by canceling the noise using Means of Clean Character Images (13) and cleaned character images extracts the height and width of the character through Means of Extraction of character height (14) for obtaining plurality of zones containing multiple average and maximum traits which are analyzed and complied as Output 3.1, 3.2 and 3.3.
• The feature of individual letters calculation of individual characters which is examined from the words extracted through said Means of OCR (Optical Character Recognition) Model (4) and extracts the bounding box coordinates of the extracted text through Means of Bounding box of words and text extraction (15) which are cropped through Means of Cropping (16) and then sent to Means of Words Pre-processing (17) for elimination of any extraneous white portions from the word to obtain clean images which are forwarded to Means of Extraction and Cropping characters (18) for extracting and cropping the individual character from the clean words and forwarded to Means of Character pre-processing (19) for removal of any extra white portion from the extracted character obtaining clean character images, said images are collected and created a separate list for each character type through Means of Character collection (20), the connection between two characters are extracted and traits are found accordingly, said unique traits of each character and the connection between two characters, are listed and sent as Output 4.
• Different trait outputs (Output-1, Output-2, Output-3, and Output-4) from said Means of Analyzing Handwriting Traits (A) are forwarded to said Means of Finalizing output (F) and it comprises of:
• Means of Compiling, Processing and Stacking Output (21) and;
• Means of Displaying Final Output (22);
• Wherein;
• Means of Compiling, Processing and Stacking Output (21) assemble different trait outputs (Output-1, Output-2, Output-3, and Output-4) for compiling, processing and stacking the received outputs for generation of final output as report containing all the compiled data and displayed on digital devices through Means of Displaying Final Output (22).