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.gitattributes

@@ -40,3 +40,4 @@ app/images/idcards/d.jpg filter=lfs diff=lfs merge=lfs -text
 app/images/idcards/driving[[:space:]]licence[[:space:]]backside[[:space:]]image.jpg filter=lfs diff=lfs merge=lfs -text
 app/images/idcards/driving[[:space:]]licence[[:space:]]frontside[[:space:]]image.jpg filter=lfs diff=lfs merge=lfs -text
 app/images/idcards/front.jpg filter=lfs diff=lfs merge=lfs -text
+app/NER/model-best/tok2vec/model filter=lfs diff=lfs merge=lfs -text

app/NER/NER.py

@@ -0,0 +1,31 @@
+import spacy
+from spacy.tokens import DocBin
+from tqdm import tqdm
+from app.ocr_engine.ocr import OCR
+from paddleocr import PaddleOCR
+
+nlp_ner = spacy.load("app/NER/model-best")
+nlp = spacy.blank("en") # load a new spacy model
+db = DocBin() # create a DocBin object
+ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
+
+def name_entity_recognizer(img_path, dictionary):
+    try:
+        ocr_list = []
+        ocr_list = ocr.ocr(img_path, cls=True)
+        text = ""
+        for word in ocr_list:
+            for line in word:
+                text += line[1][0] + '\n'
+        
+        doc = nlp_ner(text)
+        for entity in doc.ents:
+            print(entity.text, entity.label_)
+            dictionary[entity.label_] = entity.text
+        
+        
+        return dictionary, True, ocr_list
+    except Exception as e:
+        print("[Exception in name_entity_recognizer] : ", str(e))
+        return dictionary, False, ocr_list
+    # spacy.displacy.render(doc, style="ent", jupyter=True)

app/NER/model-best/config.cfg

@@ -0,0 +1,144 @@
+[paths]
+train = "./training_data.spacy"
+dev = "./training_data.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = null
+seed = 0
+
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","ner"]
+batch_size = 1000
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.ner]
+factory = "ner"
+incorrect_spans_key = null
+moves = null
+scorer = {"@scorers":"spacy.ner_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.ner.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.ner.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+upstream = "*"
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode.width}
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,1000,2500,2500]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[training]
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+accumulate_gradient = 1
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = []
+annotating_components = []
+before_to_disk = null
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_words.v1"
+discard_oversize = false
+tolerance = 0.2
+get_length = null
+
+[training.batcher.size]
+@schedules = "compounding.v1"
+start = 100
+stop = 1000
+compound = 1.001
+t = 0.0
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = false
+eps = 0.00000001
+learn_rate = 0.001
+
+[training.score_weights]
+ents_f = 1.0
+ents_p = 0.0
+ents_r = 0.0
+ents_per_type = null
+
+[pretraining]
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.tokenizer]

app/NER/model-best/meta.json

@@ -0,0 +1,76 @@
+{
+  "lang":"en",
+  "name":"pipeline",
+  "version":"0.0.0",
+  "spacy_version":">=3.5.3,<3.6.0",
+  "description":"",
+  "author":"",
+  "email":"",
+  "url":"",
+  "license":"",
+  "spacy_git_version":"Unknown",
+  "vectors":{
+    "width":0,
+    "vectors":0,
+    "keys":0,
+    "name":null,
+    "mode":"default"
+  },
+  "labels":{
+    "tok2vec":[
+
+    ],
+    "ner":[
+      "COUNTRY",
+      "DOB",
+      "DOCUMENT NUMBER",
+      "GENDER",
+      "NAME"
+    ]
+  },
+  "pipeline":[
+    "tok2vec",
+    "ner"
+  ],
+  "components":[
+    "tok2vec",
+    "ner"
+  ],
+  "disabled":[
+
+  ],
+  "performance":{
+    "ents_f":0.9238653001,
+    "ents_p":0.9131693198,
+    "ents_r":0.9348148148,
+    "ents_per_type":{
+      "COUNTRY":{
+        "p":0.8895027624,
+        "r":0.9096045198,
+        "f":0.8994413408
+      },
+      "NAME":{
+        "p":0.8896103896,
+        "r":0.9256756757,
+        "f":0.9072847682
+      },
+      "DOB":{
+        "p":0.9692982456,
+        "r":0.9692982456,
+        "f":0.9692982456
+      },
+      "GENDER":{
+        "p":0.8837209302,
+        "r":0.95,
+        "f":0.9156626506
+      },
+      "DOCUMENT NUMBER":{
+        "p":0.8705882353,
+        "r":0.9024390244,
+        "f":0.8862275449
+      }
+    },
+    "tok2vec_loss":146.4440149354,
+    "ner_loss":2517.651363954
+  }
+}

app/NER/model-best/ner/cfg

@@ -0,0 +1,13 @@
+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":1,
+  "learn_tokens":false,
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":0.0,
+  "incorrect_spans_key":null
+}

app/NER/model-best/ner/moves

@@ -0,0 +1 @@
+��moves�
D{"0":{},"1":{"DOB":419,"NAME":259,"COUNTRY":216,"DOCUMENT NUMBER":161,"GENDER":40},"2":{"DOB":419,"NAME":259,"COUNTRY":216,"DOCUMENT NUMBER":161,"GENDER":40},"3":{"DOB":419,"NAME":259,"COUNTRY":216,"DOCUMENT NUMBER":161,"GENDER":40},"4":{"DOB":419,"NAME":259,"COUNTRY":216,"DOCUMENT NUMBER":161,"GENDER":40,"":1},"5":{"":1}}�cfg��neg_key�

app/NER/model-best/tok2vec/cfg

@@ -0,0 +1,3 @@
+{
+
+}

app/NER/model-best/tok2vec/model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f387d275038876a0f7051fc91e64184446b48f3dd099832ef90ac0e806a8afc5
+size 6009091

app/NER/model-best/tokenizer

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+  "x'x",
+  "x'xx",
+  "x'xxxx",
+  "x(x",
+  "x-dd",
+  "x.",
+  "x.X",
+  "x.d",
+  "x.d.x.dd",
+  "x.x",
+  "x.x.",
+  "x.x.(xxxx",
+  "x.x.dddd",
+  "x.x.x",
+  "x.x.x.ddxxx.d",
+  "x.xx",
+  "x.xx-dd.ddd.ddd",
+  "x.xxx",
+  "x.xxxx",
+  "x.xy",
+  "x//x",
+  "x/d",
+  "x/x",
+  "x0001001",
+  "xD",
+  "xDD",
+  "xX",
+  "xXX",
+  "xXd",
+  "xXx-",
+  "xXxx",
+  "xXxxx",
+  "xXxxxx",
+  "x]XXXX",
+  "x]Xxxx",
+  "x]Xxxxx",
+  "x]xxxx",
+  "x_X",
+  "x_d",
+  "x_x",
+  "xd",
+  "xdXXXX",
+  "xdd",
+  "xdd.d",
+  "xdd/dd/dddd",
+  "xddd",
+  "xdddXx",
+  "xdddd",
+  "xdddd:dd",
+  "xddddxd",
+  "xdddxddxx",
+  "xdddxx",
+  "xddx",
+  "xddxddxdd",
+  "xddxdxxdd",
+  "xdxddddx",
+  "xdxddddxd",
+  "xdxxxx",
+  "xdxxxxddxxxxd",
+  "xex",
+  "xf",
+  "xk",
+  "xoe",
+  "xwx",
+  "xx",
+  "xx#dddd",
+  "xx'",
+  "xx'x",
+  "xx'xx",
+  "xx,dddd",
+  "xx-dd",
+  "xx.",
+  "xx.dddd",
+  "xx/",
+  "xxXddd",
+  "xxXxx",
+  "xxXxxx",
+  "xxXxxxXxxxx",
+  "xx[x",
+  "xxd",
+  "xxdd",
+  "xxdddd",
+  "xxdddxx",
+  "xxdddxxx",
+  "xxdxxdddxxxx-dxxx",
+  "xxx",
+  "xxx!xxxx",
+  "xxx'x",
+  "xxx'xxxx",
+  "xxx-",
+  "xxx-dd",
+  "xxx-dddd",
+  "xxx.xx",
+  "xxx.xxxx",
+  "xxx.xxxx.xxx",
+  "xxx.xxxx.xxx.xx",
+  "xxx/",
+  "xxx/xx",
+  "xxx:d",
+  "xxx:dd",
+  "xxx:dd/dd/dddd",
+  "xxxX",
+  "xxxXxxxx",
+  "xxxd",
+  "xxxdXxxx",
+  "xxxdd",
+  "xxxdd/dd/dddd",
+  "xxxddd",
+  "xxxdddd",
+  "xxxdddxdd",
+  "xxxdxdxd",
+  "xxxdxx",
+  "xxxdxxx",
+  "xxxdxxxx",
+  "xxxx",
+  "xxxx'",
+  "xxxx'x",
+  "xxxx'xxxx",
+  "xxxx(Xxxxx",
+  "xxxx(dxxxx",
+  "xxxx(x",
+  "xxxx(xx",
+  "xxxx(xxxx",
+  "xxxx(xxxx(x)xxxx",
+  "xxxx(xxxx(xxxx(x",
+  "xxxx(xxxx(xxxx(xxxx(xxxx",
+  "xxxx(xxxx)xxxx(xxxx",
+  "xxxx)d",
+  "xxxx**xxxx",
+  "xxxx*xxx",
+  "xxxx+",
+  "xxxx,dddd",
+  "xxxx-d",
+  "xxxx-dXddXXX",
+  "xxxx-dxddxxx",
+  "xxxx.dddd",
+  "xxxx.x",
+  "xxxx.xx",
+  "xxxx.xxx",
+  "xxxx.xxx/xxxx",
+  "xxxx.xxxx",
+  "xxxx/",
+  "xxxx:d",
+  "xxxx:dd",
+  "xxxx:dd/dd/dddd",
+  "xxxx:dddd",
+  "xxxx<<<<xxx",
+  "xxxx<<xxxx",
+  "xxxx?xxxx",
+  "xxxx@xxxx.xxx.xx",
+  "xxxxX(dxxxx",
+  "xxxxX+",
+  "xxxxXx",
+  "xxxxXxxx",
+  "xxxxXxxxx",
+  "xxxxXxxxxXxxxxX",
+  "xxxxd",
+  "xxxxdd",
+  "xxxxdddd",
+  "xxxxdddd.xxxx.x",
+  "xxxxddddxxddxxdd",
+  "xxxxddddxxdxx",
+  "xxxxddxdd",
+  "xxxxddxxdd",
+  "xxxxddxxx",
+  "xxxxddxxxdddd",
+  "xxxxdx",
+  "xxxxdxx",
+  "xxxxdxxdddd",
+  "xxxxdxxx",
+  "xxxxdxxxx",
+  "xxxx|",
+  "xxxx|xxxx|xxxx",
+  "xxxx\u2019",
+  "xxxx\u2019x",
+  "xxx\u2019x",
+  "xx\u2019",
+  "xx\u2019x",
+  "xx\u2019xx",
+  "x\u2019",
+  "x\u2019x",
+  "x\u2019xxxx",
+  "x\ufe35x",
+  "y",
+  "y'",
+  "y's",
+  "y.",
+  "ya",
+  "yad",
+  "yal",
+  "yasmin",
+  "yasmine",
+  "ybro",
+  "yed",
+  "yen",
+  "yer",
+  "yes",
+  "ygar",
+  "yit",
+  "yluce",
+  "yof",
+  "yok",
+  "york",
+  "yoti",
+  "you",
+  "younus",
+  "ype",
+  "ysN",
+  "ysc",
+  "ysn",
+  "yzx211v11",
+  "y\u2019",
+  "y\u2019s",
+  "z",
+  "z.",
+  "z3xwmx12xwms2",
+  "za0000089",
+  "zen",
+  "zid",
+  "zor",
+  "zra",
+  "zzU319",
+  "zzc",
+  "zzu319",
+  "|",
+  "}",
+  "\u00a0",
+  "\u00ac",
+  "\u00ac_\u00ac",
+  "\u00af",
+  "\u00af\\(x)/\u00af",
+  "\u00af\\(\u30c4)/\u00af",
+  "\u00b0",
+  "\u00b0C.",
+  "\u00b0F.",
+  "\u00b0K.",
+  "\u00b0X.",
+  "\u00b0c.",
+  "\u00b0f.",
+  "\u00b0k.",
+  "\u00b0x.",
+  "\u00e4",
+  "\u00e4.",
+  "\u00f6",
+  "\u00f6.",
+  "\u00fc",
+  "\u00fc.",
+  "\u0ca0",
+  "\u0ca0_\u0ca0",
+  "\u0ca0\ufe35\u0ca0",
+  "\u2014",
+  "\u2018",
+  "\u2018S",
+  "\u2018X",
+  "\u2018s",
+  "\u2018x",
+  "\u2019",
+  "\u2019-(",
+  "\u2019-)",
+  "\u2019Cause",
+  "\u2019Cos",
+  "\u2019Coz",
+  "\u2019Cuz",
+  "\u2019S",
+  "\u2019X",
+  "\u2019Xxx",
+  "\u2019Xxxxx",
+  "\u2019am",
+  "\u2019bout",
+  "\u2019cause",
+  "\u2019cos",
+  "\u2019coz",
+  "\u2019cuz",
+  "\u2019d",
+  "\u2019em",
+  "\u2019ll",
+  "\u2019m",
+  "\u2019nuff",
+  "\u2019re",
+  "\u2019s",
+  "\u2019ve",
+  "\u2019x",
+  "\u2019xx",
+  "\u2019xxx",
+  "\u2019xxxx",
+  "\u2019y",
+  "\u2019\u2019",
+  "\u2501",
+  "\u253b",
+  "\u253b\u2501\u253b",
+  "\u256f",
+  "\u25a1",
+  "\ufe35",
+  "\uff09"
+]

app/NER/model-best/vocab/vectors.cfg

@@ -0,0 +1,3 @@
+{
+  "mode":"default"
+}

app/extract_gender/gender_extractor.py

@@ -6,17 +6,22 @@ def gender_extract(ocr_list):
             for word in words.split(): 
                 word = word.upper()
                 if word in gender_labels:
+                    # ocr_list.pop(word)
                     return True, word, ocr_list
                 elif "FEMALE" in word:
+                    # ocr_list.pop(word)
                     gender = 'F'
                     return True, gender, ocr_list
                 elif "MALE" in word:
+                    # ocr_list.pop(word)
                     gender = 'M'
                     return True, gender, ocr_list
                 elif "FEMENINO" in word:
+                    # ocr_list.pop(word)
                     gender = "F"
                     return True, gender, ocr_list
                 elif "MASCULINA" in word:
+                    # ocr_list.pop(word)
                     gender = "M"
                     return True, gender, ocr_list
                 

app/functions.py

@@ -0,0 +1,55 @@
+from PIL import Image, ImageFilter
+import cv2
+import pandas as pd
+
+def get_index(string, substring):
+    
+    start_index = string.find(substring)  # Starting index of "shahzain"
+    end_index = start_index + len(substring)  # Ending index of "shahzain"
+    
+    return int(start_index), int(end_index)
+
+def get_latest_value_from_csv(search_key):
+    try:
+        output_dict = {}
+        df = pd.read_csv("/content/log.csv")
+        filtered_df = df[df["OCR Extracted Text"] == search_key]
+        # Sort the filtered DataFrame by the timestamp column in descending order
+        sorted_df = filtered_df.sort_values("timestamp", ascending=False)
+        if not filtered_df.empty:
+            # Extract the latest row
+            latest_row = sorted_df.iloc[0]
+            # Print the values of specific columns that are not null
+            columns_to_print = ["NAME", "DOB", "GENDER", "COUNTRY", "DOCUMENT NUMBER"]
+            for column in columns_to_print:
+                if pd.notnull(latest_row[column]):
+                    output_dict[column] = latest_row[column]
+            print("Training data from user", output_dict)
+        else:
+            print("Annotated Data Not Found")
+        
+        return True, output_dict
+    except Exception as e:
+        print("Exception in e ", str(e))
+        return False, output_dict
+    
+def image_enhacement(file_path):
+
+    image = Image.open(file_path)
+    upscaled_image = image.resize((image.width*2, image.height*2), resample=Image.Resampling.BILINEAR)
+    sharpened_image = upscaled_image.filter(ImageFilter.SHARPEN)
+    sharpened_image.save(file_path)
+    
+    sharpened_image = cv2.imread(file_path)
+    smoothed_image = cv2.GaussianBlur(sharpened_image, (5, 5), 0)     # Apply smoothing
+    
+    # Set the desired output level range
+    output_min = 55
+    output_max = 255
+
+    # Calculate the minimum and maximum pixel values in the image
+    # min_value = np.min(image)
+    # max_value = np.max(image)
+
+    normalized_image = cv2.normalize(smoothed_image, None, output_min, output_max, cv2.NORM_MINMAX) # Normalize the image to the desired output level range
+    cv2.imwrite(file_path, normalized_image)

app/training_data/annotated_data.json

@@ -0,0 +1,28 @@
+{
+    "classes": [
+        "NAME",
+        "COUNTRY",
+        "GENDER",
+        "DOB",
+        "DOCUMENT NUMBER"
+    ],
+    "annotations": [
+        [
+            "PAKISTAN Nationalldentity Card ISLAMIC REPUBLIC OF PAKISTAN Name Muhammad Owais Siddiqui Father Name Zia Ud Din Siddiqui Country of Stay Gender M Pakistan Identity Number Date of Birth 42201-1177064-3 11.08.2001 Date of Expiry Date of Issue 24.09.2019 24.09.2029 Holder's Signature",
+            {
+                "entities": [
+                    [
+                        65,
+                        88,
+                        "NAME"
+                    ],
+                    [
+                        185,
+                        200,
+                        "DOCUMENT NUMBER"
+                    ]
+                ]
+            }
+        ]
+    ]
+}

deploy.py

@@ -1,22 +1,26 @@
 import gradio as gr
-import numpy as np
+from app.NER.NER import name_entity_recognizer
 from app.extract_country.country_dictionary import find_country
 from app.extract_dates.date_engine import date_extractor
 from app.extract_gender.gender_extractor import gender_extract
 from app.extract_identity_number.doc_number_extractor import doc_number
-from app.ocr_engine.ocr import OCR
-from paddleocr import PaddleOCR
-from app.layoutLM_api.api import custom_ocr
 import cv2
-from PIL import Image, ImageFilter
+from app.functions import get_index, get_latest_value_from_csv, image_enhacement
+import json
 
 
-def ocr(image_input, webcam_input, card_type_input):
+def ocr(card_type_input, image_input, webcam_input):
+    global dictionary
 
-    file_path = 'app/images/idcards/input.jpg'
+    dictionary = {
+        "NAME": "",
+        "DOB": "",
+        "COUNTRY": "",
+        "GENDER": "",
+        "DOCUMENT NUMBER": "",
+    }
 
-    print("image_input : ", type(image_input))
-    print("Webcam Image : ", type(webcam_input))
+    file_path = "app/images/idcards/input.jpg"
 
     if image_input is not None and image_input.any():
         print("In input image")
@@ -25,80 +29,161 @@ def ocr(image_input, webcam_input, card_type_input):
     else:
         print("In webcam")
         cv2.imwrite(file_path, cv2.flip(webcam_input, 1))
-    
-    image = Image.open(file_path)
-    upscaled_image = image.resize((image.width*2, image.height*2), resample=Image.Resampling.BILINEAR)
-    sharpened_image = upscaled_image.filter(ImageFilter.SHARPEN)
-    sharpened_image.save(file_path)
-    
-    sharpened_image = cv2.imread(file_path)
-    # Apply smoothing
-    smoothed_image = cv2.GaussianBlur(sharpened_image, (5, 5), 0)
-    # Set the desired output level range
-    output_min = 55
-    output_max = 255
-
-    # Calculate the minimum and maximum pixel values in the image
-    min_value = np.min(image)
-    max_value = np.max(image)
-
-    # Normalize the image to the desired output level range
-    normalized_image = cv2.normalize(smoothed_image, None, output_min, output_max, cv2.NORM_MINMAX)
-
 
-    # Save the upscaled, smoothed, and sharpened image
-    cv2.imwrite(file_path, normalized_image)
+    image_enhacement(file_path)
+    # print("file_path", file_path)
+    dictionary, NER_check, result = name_entity_recognizer(file_path, dictionary)
 
-    # print(type(input_img))
-    ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
+    extract_text = []
+    extract_text = [line[1][0] for res in result for line in res]
+    extract_text = " ".join(extract_text)
+    print("extract_text", extract_text)
 
-    # with open(file_path, "wb+") as file_object:
-    #     file_object.write(input_img.file.read())
-    
-    dictionary = custom_ocr(file_path, card_type_input)
-
-    if '' in list(dictionary.values()):
+    if "" in list(dictionary.values()):
         print("Missing value found in Dic")
-        
-        extract_text = []
-        result = ocr.ocr(file_path, cls=True)
 
-        extract_text = [line[1][0] for res in result for line in res]
-        print("extract_text", extract_text)
-
-        if len(dictionary['gender']) == 0:
+        if len(dictionary["GENDER"]) == 0:
             print("Gender Missing")
             gender_found, gender, ocr_list = gender_extract(extract_text)
-            
-            dictionary["gender"] = gender if gender_found else None
 
-        if len(dictionary['dob']) == 0:
+            dictionary["GENDER"] = gender if gender_found else None
+
+        if len(dictionary["DOB"]) == 0:
             print("Dob Missing")
             dob_found, dob, ocr_list = date_extractor(extract_text)
 
-            dictionary["dob"] = dob if dob_found else None
+            dictionary["DOB"] = dob if dob_found else None
 
-        if len(dictionary['country']) == 0:
+        if len(dictionary["COUNTRY"]) == 0:
             print("Country Missing")
             country_found, country, ocr_list = find_country(extract_text)
             dictionary["country"] = country if country_found else None
 
-        if len(dictionary['document_number']) == 0:
+        if len(dictionary["DOCUMENT NUMBER"]) == 0:
             print("document Number missing")
 
             document_number_found, document_number, ocr_list = doc_number(extract_text)
-            dictionary["document_number"] = document_number if document_number_found else None
+            dictionary["DOCUMENT NUMBER"] = (
+                document_number if document_number_found else None
+            )
+
+    # bool_check , training_data = get_latest_value_from_csv(extract_text)
+    # print(training_data)
+    print("Updated Dict ", dictionary)
+    response = {"Status": 200, "OCR": dictionary}
+    return response, extract_text
+
+
+def save_traning_data(
+    name_input, dob_input, country_input, gender_input, doc_num_input,string_output
+):
+    print(name_input, dob_input, country_input, gender_input, doc_num_input)
+    print("Saving data")
+    # print(string_output)
+    entities_list = []
+    
+    if name_input:
+        entry = []
+        start_index, end_index = get_index(string_output, name_input)
+        entry.append(start_index)
+        entry.append(end_index)
+
+        entry.append("NAME")
 
+        entities_list.append(entry)
 
-    print("Updated Dict ",dictionary)
-    response = {"Status" : 200, "OCR" : dictionary}  
-    return response
- 
-# Define the input objects
-image_input = gr.inputs.Image(label="Upload Image")
-webcam_input = gr.inputs.Image(label="Webcam", source="webcam")
-card_type_input = gr.inputs.Dropdown(["passport", "id_card"], label="Card Type")
+    if dob_input:
+        entry = []
+        start_index, end_index = get_index(string_output, dob_input)
+        entry.append(start_index)
+        entry.append(end_index)
+        entry.append("DOB")
 
-# Create the Gradio interface
-interface = gr.Interface(fn=ocr, inputs=[image_input, webcam_input, card_type_input], outputs="json")
-interface.launch()
+        entities_list.append(entry)
+    
+    if country_input:
+        entry = []
+        start_index, end_index = get_index(string_output, country_input)
+        entry.append(start_index)
+        entry.append(end_index)
+        entry.append("COUNTRY")
+
+        entities_list.append(entry)
+    
+    if gender_input:
+        entry = []
+        start_index, end_index = get_index(string_output, gender_input)
+        entry.append(start_index)
+        entry.append(end_index)
+        entry.append("GENDER")
+
+        entities_list.append(entry)
+    
+    if doc_num_input:
+        entry = []
+        start_index, end_index = get_index(string_output, doc_num_input)
+        entry.append(start_index)
+        entry.append(end_index)
+        entry.append("DOCUMENT NUMBER")
+        
+        entities_list.append(entry)
+        print("entities_list" , entities_list)
+
+    with open("app/training_data/annotated_data.json") as f:
+        data = json.load(f)
+        
+        new_list = [
+            string_output,
+            {
+                "entities": entities_list
+            }
+        ]
+
+        annotations = data["annotations"]
+        annotations.append(new_list)
+    
+        with open('app/training_data/annotated_data.json', 'w') as file:
+            json.dump(data, file)
+
+# text = "PAKISTAN Nationalldentity Card ISLAMIC REPUBLIC OF PAKISTAN Name Muhammad Owais Siddiqui Father Name Zia Ud Din Siddiqui Country of Stay Gender M Pakistan Identity Number Date of Birth 42201-1177064-3 11.08.2001 Date of Expiry Date of Issue 24.09.2019 24.09.2029 Holder's Signature"
+# save_traning_data(
+#     "Muhammad Owais Siddiqui", "Card", "REPUBLIC", "PAKISTAN", "Nationalldentity",text
+# )
+
+with gr.Blocks() as interface:
+    with gr.Row():
+        with gr.Column():
+            card_type_input = gr.inputs.Dropdown(
+                ["passport", "id_card"], label="Card Type", default="id_card"
+            )
+            image_input = gr.inputs.Image(label="Upload Image")
+            webcam_input = gr.inputs.Image(label="Webcam", source="webcam")
+        with gr.Column():
+            # Define the output components
+            json_output = gr.outputs.JSON(label="JSON Output")
+            string_output = gr.outputs.Textbox(label="OCR Extracted Text")
+            ocr_btn = gr.Button("Process OCR")
+
+            name_input = gr.inputs.Textbox(label="NAME", optional=True, type="text")
+            dob_input = gr.inputs.Textbox(label="DOB", optional=True, type="text")
+            country_input = gr.inputs.Textbox(
+                label="COUNTRY", optional=True, type="text"
+            )
+            gender_input = gr.inputs.Textbox(label="GENDER", optional=True, type="text")
+            doc_num_input = gr.inputs.Textbox(
+                label="DOCUMENT NUMBER", optional=True, type="text"
+            )
+            save_btn = gr.Button("Save Data")
+
+    ocr_btn.click(
+        fn=ocr,
+        inputs=[card_type_input, image_input, webcam_input],
+        outputs=[json_output, string_output],
+    )
+
+    save_btn.click(
+        fn=save_traning_data,
+        inputs=[name_input, dob_input, country_input, gender_input, doc_num_input,string_output],
+    )
+
+interface.launch(share=True, show_tips=True, debug=True)

requirements.txt

@@ -17,16 +17,19 @@ azure-mgmt-rdbms==10.1.0
 azure-mgmt-resource==23.0.0
 azure-mgmt-subscription==3.1.1
 Babel==2.12.1
+backports.zoneinfo==0.2.1
 bce-python-sdk==0.8.83
 bcrypt==4.0.1
 beautifulsoup4==4.12.2
 bidict==0.22.1
 blinker==1.6.2
+blis==0.7.9
 boto3==1.26.142
 botocore==1.29.142
 Brotli==1.0.9
 butler-sdk==1.0.0
 cachetools==5.3.0
+catalogue==2.0.8
 certifi==2023.5.7
 cffi==1.15.1
 charset-normalizer==3.1.0
@@ -34,11 +37,13 @@ click==8.1.3
 cmake==3.26.3
 colorama==0.4.6
 colorlog==6.7.0
+confection==0.0.4
 contourpy==1.0.7
 cryptography==40.0.2
 cssselect==1.2.0
 cssutils==2.6.0
 cycler==0.11.0
+cymem==2.0.7
 Cython==0.29.34
 datasets==2.12.0
 decorator==5.1.1
@@ -102,6 +107,7 @@ jmespath==1.0.1
 joblib==1.2.0
 jsonschema==4.17.3
 kiwisolver==1.4.4
+langcodes==3.3.0
 lanms-neo==1.0.2
 lazy_loader==0.2
 ldap3==2.9.1
@@ -122,6 +128,7 @@ msal-extensions==1.0.0
 msrest==0.7.1
 multidict==6.0.4
 multiprocess==0.70.12.2
+murmurhash==1.0.9
 networkx==3.1
 numpy==1.24.3
 oauthlib==3.2.2
@@ -140,6 +147,7 @@ paddlepaddle==2.4.2
 pandas==2.0.1
 paramiko==3.1.0
 passlib==1.7.4
+pathy==0.10.1
 pdf2docx==0.5.6
 pdf2image==1.16.3
 Pillow==9.5.0
@@ -147,6 +155,7 @@ pkgutil_resolve_name==1.3.10
 Polygon3==3.0.9.1
 portalocker==2.7.0
 premailer==3.10.0
+preshed==3.0.8
 protobuf==3.19.6
 psutil==5.9.5
 psycopg==3.1.8
@@ -198,15 +207,21 @@ sentencepiece==0.1.99
 seqeval==1.2.2
 shapely==2.0.1
 six==1.16.0
+smart-open==6.3.0
 sniffio==1.3.0
 soupsieve==2.4.1
+spacy==3.5.3
+spacy-legacy==3.0.12
+spacy-loggers==1.0.4
 speaklater3==1.4
 SQLAlchemy==2.0.15
 sqlparse==0.4.4
+srsly==2.4.6
 sshtunnel==0.4.0
 starlette==0.27.0
 sympy==1.12
 termcolor==2.3.0
+thinc==8.1.10
 threadpoolctl==3.1.0
 tifffile==2023.4.12
 tokenizers==0.13.3
@@ -216,7 +231,7 @@ tqdm==4.65.0
 transformers==4.29.2
 triton==2.0.0
 tritonclient==2.33.0
-typer==0.9.0
+typer==0.7.0
 typing_extensions==4.5.0
 tzdata==2023.3
 ua-parser==0.16.1
@@ -226,6 +241,7 @@ urllib3==1.26.16
 user-agents==2.2.0
 uvicorn==0.22.0
 visualdl==2.4.2
+wasabi==1.1.2
 websockets==11.0.3
 Werkzeug==2.2.3
 WTForms==3.0.1

run.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from deploy import ocr_btn
+import pandas as pd
+
+
+with gr.Blocks() as demo:
+    image_input = gr.inputs.Image(label="Upload Image")
+    webcam_input = gr.inputs.Image(label="Webcam", source="webcam")
+    card_type_input = gr.inputs.Dropdown(["passport", "id_card"], label="Card Type", default="id_card")
+
+    # column_names = ["name", "dob", "gender", "country", "doc_num"]
+    # input_table = gr.inputs.Table(columns=column_names)
+    data = {
+    "Name": ["John", "Emma", "Michael"],
+    "Age": [25, 30, 35],
+    "Country": ["USA", "Canada", "UK"]
+    }
+    df = pd.DataFrame(data)
+
+    table_input = gr.inputs.Table(headers=list(df.columns), row_count=len(df), default_data=df.values.tolist())
+
+
+    # Define the output components
+    json_output = gr.outputs.JSON(label="JSON Output")
+    string_output = gr.outputs.Textbox(label="OCR Extracted Text")
+
+    # Create the Gradio interface
+    interface = gr.Interface(fn=ocr_btn, inputs=[card_type_input,image_input,webcam_input], outputs=[json_output, string_output])
+    interface.launch(share=True)
+
+if __name__ == "__main__":
+    demo.launch()